In 2009, we completed this project as part of our Post Graduate program, and it's fascinating to see how the mobile industry has undergone significant advancements since that time.¶

Chapter 1¶

Introduction¶

Mobile means that moves by itself. Mobile phones are becoming more and more common that life may be unimaginable without them .Carrying a mobile phone makes most people feel more secure, especially students when they are not with their parents. Our investigation will be to study whether this widespread use of mobile phones playing an important role in the student population or not.

Students use mobiles either it is a necessity or they can have a mobile or they are using for prestige etc. Our project mainly concentrates on that what factors influencing in selecting a handset, and to know that why students are using mobiles.

The usage of mobile is mainly based on two things one is handset and the other is service provider. Our survey considers these two in analyzing the mobile phone usage among stu- dents. The popular handset brands are NOKIA, LG, Samsung, Sony Ericsson and Motorola. And the popular service provider in Andhar Pradesh, India are Airtel, BSNL, idea, Vodafone, Reliance, Tata and Virgin.

Among handset brands NOKIA has larger market share and Airtel has larger market among service provider. Year by year the call charges and rentals are declining. This causes the increase of mobile usage. The handset costs are also declining then purchasing capacity increases. Usually the mobile usage population increases among students. Hence, we are interested to know that what makes a student select a mobile.

1.1 Objectives¶

  1. To test whether there is any association between handset cost and income.
  2. To test whether there is any association between monthly mobile expenditure and income.
  3. What are the factors that influence the selection of a handset.
  4. What for students are using mobile phones.

1.2 Sample selection¶

As the mobile using student population is very large we have selected different categories of students such as intermediate, degree, post graduates and research scholors. And our sample included both males and females, rural and urban of the above categories. The only criterion that a student will be included in the sample is that he should have a mobile. We approached students on their campuses and hostels. The questionnaire was given to the students who has mobiles after enquiring them whether they have a mobile or not. Even though we have not used any standard sampling technique it may be treated as random sample because the sample size is large and included different categories.

Field and other experiences¶

Unbalanced sample¶

Even though our sample included both males and females the proportion is not a balanced one. Male participants are more than two times of females. And various categories of students included in the sample, but the proportion of P.G. students is more than the others,the proportion of male students is greater than females and the proportion of rural students is greater than urban students.

Design of questionnaire¶

In the design of questionnaire the variables meant for factor analysis are not enough for some questions. There are some ambiguous questions which are meant for factor analysis. Obviously some students felt inconvenience while responding to them.

Lengthy questionnaire¶

Some students felt that they have spent much time in completing the questionnaire. And some students rejected to fill the questionnaire feeling that it will take more time. But we have included the questions which are necessary to this type of survey.

Not aware of terminology and technology¶

Some of the students do not know the difference between handset and service provider. Some of them are not aware of such a modern features like bluetooth, camera pixels etc

Participant’s nature¶

Most of the students filled questionnaire willingly and some of them seem to be participated just to fill it. Some of them gave valuable suggestions to us while proceeding for further survey and analysis.

1.3 Statistical Techniques And Packages¶

To analyze our data, we have used some statistical tools which can analyze our data as much as possible. The tools we have used are cross tables, statistics associated with cross tables and factor analysis. The software we have used to analyze the data is SPSS.

1.3.1 Cross tables¶

As our sample consists of number of variables and some of them measured simultaneously, then a naturally arising question is ”Is there any association among the variables.” To test the association among them we have used the concept of cross tables. Generally used test statistic to test the association among variables is Chi-square test of independence. This test can tell us whether there is an association or not. But it cannot give us any indication as to the direction of association or the strength of association. It can tell only whether there is association or not. We need to measure the strength and direction of association. There are some other test procedures to know the direction and strength of association. These procedures are classified into various groups depending on the measurement level of the variables. The nominal and ordinal measures are used to test the nominal and ordinal data respectively. As our data is ordinal in nature we have used such ordinal measures to test and measure the association. And they are described in the following chapter and some nominal measures also.

1.3.2 Factor analysis¶

Factor analysis describes the covariance relationships among several variables in terms of a few underlying unobservable random quantities called factors. Factor analysis can be considered as an extension of principal component analysis. Both the techniques attempt to approximate the covariance matrix. In factor analysis, we seek to account for the covariance or correlations among the variables. Dimension reduction and interpretation are two basic objectives of factor analysis. There are several methods that can be used to extract factors and the principal component method is one such method. We use this method for extracting factors. After factor extraction, we rotate the solution so that we can give some interpretation to the extracted factors. Again there are quite a number of rotation techniques available. These rotation techniques can be grouped into orthogonal and non-orthogonal. In the present context orthogonal rotation is appropriate. The method we have chosen is Varimax.

In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

from scipy.stats import chi2_contingency

from tabulate import tabulate

#import scipy as spy
#import sklearn as sk
In [2]:
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
In [3]:
data = pd.read_csv('Project2009SPSS_to_CSV.csv')
In [4]:
# Define a function to highlight a specific cell
# Define a function to highlight a specific cell
def highlight_cell(val):
    try:
        float_val = float(val)
        color = 'background-color: yellow' if float_val >= 0.6 else ''
    except ValueError:
        color = ''
    return color

# Apply the styling function to the DataFrame
#styled_df = your_dataframe.style.applymap(highlight_cell)
In [5]:
data.head()
Out[5]:
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20 Q21-1 Q21-2 Q21-3 Q21-4 Q21-5 Q21-6 Q21-7 Q21-8 Q22-1 Q22-2 Q22-3 Q22-4 Q22-5 Q22-6 Q22-7 Q22-8 Q22-9 Q22-10 Q22-11 Q22-12 Q22-13 Q22-14 Q22-15 Q22-16 Q22-17 Q22-18 Q22-19 Q22-20 Q22-21 Q23-1 Q23-2 Q23-3 Q23-4 Q23-5 Q23-6 Q23-7 Q23-8 Q23-9 Q23-10 Q23-11 Q24-1 Q24-2 Q24-3 Q24-4 Q24-5 Q24-6 Q24-7 Q25-1 Q25-2 Q25-3 Q25-4 Q25-5 Q25-6 Q25-7 Q25-8 Q25-9 Q25-10 Q25-11 Q25-12 Q25-13
0 1 21 1 2 2 2 1 1 2 2 2 2 1 2 2 1 1 1 5 4 4 4 3 2 5 4 2 5 5 4 4 4 3 3 3 2 2 4 4 5 3 3 3 4 4 2 4 5 4 1 1 4 3 1 1 1 1 1 1 1 1 1 1 4 5 1 1 3 2 2 4 3 4 4 4 4 4.0 2 3 2
1 2 24 1 3 2 1 1 1 3 2 3 2 1 1 1 2 1 1 3 2 4 5 4 3 4 4 3 3 3 3 5 4 4 5 4 4 4 5 3 3 1 1 1 5 3 1 2 4 5 3 5 1 4 5 1 4 1 1 1 1 3 7 5 1 1 1 3 5 1 3 5 2 5 5 3 4 5.0 3 2 3
2 3 21 1 3 2 2 1 1 1 1 3 2 2 2 1 1 2 2 1 2 5 4 3 3 5 5 3 4 5 5 4 4 4 4 3 3 3 5 5 4 4 3 3 4 3 3 4 4 4 1 1 1 4 5 1 1 1 1 1 1 2 5 1 1 1 1 1 4 2 2 5 2 2 5 4 4 5.0 4 4 3
3 4 21 1 3 2 1 1 1 1 1 2 3 1 1 1 2 2 1 1 3 5 4 4 4 4 4 4 4 4 4 4 4 3 4 4 3 4 4 4 4 4 4 3 4 4 4 4 4 4 4 4 4 4 4 1 1 1 1 1 1 4 4 4 4 4 4 4 4 4 3 4 4 2 4 4 4 4.0 4 5 4
4 5 21 1 3 2 3 1 1 1 2 2 1 2 3 3 2 2 2 3 2 5 5 5 5 5 5 5 5 5 4 5 5 5 5 5 5 5 4 5 5 5 3 3 5 5 3 3 5 5 1 1 1 1 1 1 1 1 1 1 1 4 4 4 4 3 3 4 4 4 4 4 4 5 5 5 5 5.0 4 5 5
In [6]:
data.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 527 entries, 0 to 526
Data columns (total 80 columns):
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---  ------  --------------  -----  
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 31  Q22-4   527 non-null    int64  
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 72  Q25-6   527 non-null    int64  
 73  Q25-7   527 non-null    int64  
 74  Q25-8   527 non-null    int64  
 75  Q25-9   527 non-null    int64  
 76  Q25-10  526 non-null    float64
 77  Q25-11  527 non-null    int64  
 78  Q25-12  527 non-null    int64  
 79  Q25-13  527 non-null    int64  
dtypes: float64(1), int64(79)
memory usage: 329.5 KB
In [7]:
data
Out[7]:
Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20 Q21-1 Q21-2 Q21-3 Q21-4 Q21-5 Q21-6 Q21-7 Q21-8 Q22-1 Q22-2 Q22-3 Q22-4 Q22-5 Q22-6 Q22-7 Q22-8 Q22-9 Q22-10 Q22-11 Q22-12 Q22-13 Q22-14 Q22-15 Q22-16 Q22-17 Q22-18 Q22-19 Q22-20 Q22-21 Q23-1 Q23-2 Q23-3 Q23-4 Q23-5 Q23-6 Q23-7 Q23-8 Q23-9 Q23-10 Q23-11 Q24-1 Q24-2 Q24-3 Q24-4 Q24-5 Q24-6 Q24-7 Q25-1 Q25-2 Q25-3 Q25-4 Q25-5 Q25-6 Q25-7 Q25-8 Q25-9 Q25-10 Q25-11 Q25-12 Q25-13
0 1 21 1 2 2 2 1 1 2 2 2 2 1 2 2 1 1 1 5 4 4 4 3 2 5 4 2 5 5 4 4 4 3 3 3 2 2 4 4 5 3 3 3 4 4 2 4 5 4 1 1 4 3 1 1 1 1 1 1 1 1 1 1 4 5 1 1 3 2 2 4 3 4 4 4 4 4.0 2 3 2
1 2 24 1 3 2 1 1 1 3 2 3 2 1 1 1 2 1 1 3 2 4 5 4 3 4 4 3 3 3 3 5 4 4 5 4 4 4 5 3 3 1 1 1 5 3 1 2 4 5 3 5 1 4 5 1 4 1 1 1 1 3 7 5 1 1 1 3 5 1 3 5 2 5 5 3 4 5.0 3 2 3
2 3 21 1 3 2 2 1 1 1 1 3 2 2 2 1 1 2 2 1 2 5 4 3 3 5 5 3 4 5 5 4 4 4 4 3 3 3 5 5 4 4 3 3 4 3 3 4 4 4 1 1 1 4 5 1 1 1 1 1 1 2 5 1 1 1 1 1 4 2 2 5 2 2 5 4 4 5.0 4 4 3
3 4 21 1 3 2 1 1 1 1 1 2 3 1 1 1 2 2 1 1 3 5 4 4 4 4 4 4 4 4 4 4 4 3 4 4 3 4 4 4 4 4 4 3 4 4 4 4 4 4 4 4 4 4 4 1 1 1 1 1 1 4 4 4 4 4 4 4 4 4 3 4 4 2 4 4 4 4.0 4 5 4
4 5 21 1 3 2 3 1 1 1 2 2 1 2 3 3 2 2 2 3 2 5 5 5 5 5 5 5 5 5 4 5 5 5 5 5 5 5 4 5 5 5 3 3 5 5 3 3 5 5 1 1 1 1 1 1 1 1 1 1 1 4 4 4 4 3 3 4 4 4 4 4 4 5 5 5 5 5.0 4 5 5
5 6 22 1 3 3 1 1 1 2 2 3 3 1 2 3 2 1 1 4 2 4 5 4 3 5 5 3 4 5 5 4 4 5 4 3 3 3 4 5 4 5 4 4 4 4 4 4 4 4 5 1 1 4 4 4 1 3 4 1 5 5 7 5 1 1 1 1 5 4 3 4 2 2 4 5 4 4.0 5 5 3
6 7 24 1 3 2 2 1 2 1 2 2 6 1 2 4 1 1 1 2 6 4 4 1 1 4 4 1 1 5 5 4 4 4 4 3 3 3 4 4 5 5 5 2 4 4 1 4 1 4 5 5 1 1 1 1 3 1 1 1 1 7 7 6 7 1 1 7 1 4 4 5 1 1 5 5 1 1.0 1 1 1
7 8 21 1 3 2 2 1 2 1 3 1 3 2 2 1 1 1 1 1 3 5 5 3 3 5 5 3 3 5 5 5 5 5 5 3 3 3 5 5 5 4 3 3 3 5 3 4 4 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 5 3 5 5 4 4 5.0 3 4 4
8 9 21 1 3 2 1 1 1 1 3 3 1 1 1 1 1 2 1 1 1 5 4 4 4 5 4 4 4 5 5 5 5 5 4 4 4 4 5 4 5 4 4 4 5 5 5 4 4 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 4 4 4 4 5 4 5 4 4.0 3 4 4
9 10 22 1 2 4 3 1 1 4 2 3 3 2 2 2 2 1 1 3 3 5 5 3 3 5 5 3 4 4 5 4 4 3 2 3 3 3 5 5 5 3 1 1 1 2 2 4 4 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 5 2 4 5 4 4 5.0 3 4 2
10 11 22 1 3 2 1 1 1 1 1 3 3 2 1 1 1 1 1 1 3 4 3 5 5 5 3 3 2 5 5 3 3 3 3 4 4 4 4 3 3 4 2 3 4 4 3 4 2 3 1 1 1 1 1 1 1 1 1 1 1 1 2 6 1 6 1 1 3 3 2 4 3 3 5 3 2 3.0 2 3 2
11 12 21 1 2 2 2 1 1 1 1 3 3 2 2 1 1 1 2 1 3 5 4 3 3 5 5 3 4 5 5 4 4 3 3 3 3 3 5 5 4 4 3 3 3 3 3 4 4 4 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 3 2 1 5 3 4 5 5 5 5.0 3 5 3
12 13 22 1 3 2 1 1 1 5 2 3 2 1 2 1 1 2 2 4 2 5 4 4 5 4 4 2 4 5 5 5 5 5 4 4 4 4 4 4 5 5 5 5 5 5 5 5 4 5 2 1 1 1 1 1 1 1 1 1 1 5 5 5 1 3 1 5 4 2 2 5 4 4 5 4 4 5.0 2 4 4
13 14 22 1 3 4 1 2 1 1 1 2 5 2 2 4 2 1 1 4 3 4 3 4 4 5 5 4 4 5 5 5 5 5 4 4 4 4 5 5 4 4 3 4 4 3 3 4 3 3 1 1 1 1 5 1 1 1 1 1 1 1 3 1 5 6 5 5 4 3 2 5 2 2 5 4 3 3.0 3 2 1
14 15 22 1 3 4 2 2 1 1 2 3 3 3 4 4 2 1 1 2 3 5 5 5 4 3 4 4 5 4 5 5 5 5 4 4 4 3 4 4 5 5 5 5 5 5 5 5 4 4 1 5 1 1 1 1 1 1 1 1 1 3 6 1 1 5 5 6 3 3 3 4 2 2 5 5 3 5.0 4 5 3
15 16 21 1 3 2 2 1 2 1 2 1 3 1 2 2 2 2 1 2 1 5 4 3 2 4 5 3 5 5 4 3 4 4 4 2 2 2 4 5 3 3 3 3 3 3 3 4 5 4 4 1 1 1 1 1 3 1 5 1 1 2 3 1 5 3 1 1 4 4 4 3 2 2 4 2 3 5.0 5 2 3
16 17 21 0 3 4 3 2 2 2 2 2 2 2 3 4 1 1 3 5 2 5 5 5 5 5 5 5 4 3 3 3 5 4 3 4 2 1 3 5 4 1 1 2 3 2 5 2 1 1 5 1 1 1 3 1 4 1 1 1 1 5 5 5 1 1 5 1 1 1 1 1 1 1 5 1 1 1.0 1 1 1
17 18 22 1 3 4 2 2 1 1 2 3 3 3 3 3 1 1 1 1 3 4 3 3 4 3 5 3 4 4 4 4 3 3 3 3 3 3 3 4 4 4 2 3 3 2 4 4 4 3 5 1 1 1 5 1 3 2 1 1 1 1 5 2 4 3 1 1 3 3 2 5 1 2 5 4 4 3.0 3 3 2
18 19 26 1 3 4 2 1 2 2 2 3 5 4 4 4 1 1 4 4 4 2 4 4 3 4 3 3 4 3 4 4 3 2 4 3 3 3 3 2 2 3 4 4 2 2 3 3 4 4 3 1 1 2 1 1 3 1 2 3 5 4 6 6 1 5 5 6 2 3 2 5 2 3 5 4 4 3.0 3 2 2
19 20 26 1 3 4 2 1 2 5 4 2 2 3 4 4 2 1 2 3 2 5 4 3 3 4 4 3 2 3 4 3 3 2 2 4 3 2 2 2 4 1 3 2 3 4 2 1 4 2 3 1 1 3 5 1 1 5 1 5 5 4 4 4 1 1 1 4 2 3 2 5 2 2 5 4 4 3.0 3 2 3
20 21 24 1 3 2 1 2 2 2 2 2 3 1 1 1 1 2 1 4 3 3 5 3 5 3 5 1 4 5 5 5 5 5 5 4 4 4 3 3 5 1 1 1 1 1 1 1 5 5 1 1 5 1 2 4 1 1 1 3 1 5 5 5 5 5 5 5 1 1 1 3 1 1 5 3 5 3.0 1 1 1
21 22 22 0 3 4 2 2 1 3 4 3 2 4 4 4 1 1 4 4 2 3 3 3 4 5 2 3 5 5 5 4 3 3 3 3 3 3 5 3 2 2 2 2 2 2 2 3 4 3 3 3 1 1 5 1 3 2 4 5 3 1 2 3 4 5 6 7 5 5 5 2 5 5 2 3 2 2.0 5 3 2
22 23 28 1 2 2 1 1 1 1 2 3 1 1 1 1 1 2 1 1 1 4 4 4 4 4 4 4 4 4 3 3 3 3 4 3 4 4 4 4 3 3 3 4 4 4 4 4 3 3 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 3 2 5 4 4 4 4 4 3.0 2 2 2
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24 25 23 1 3 4 3 1 1 1 2 3 3 1 1 1 1 1 1 1 3 3 5 4 5 5 5 5 3 4 5 5 5 3 4 3 5 5 5 5 5 4 4 4 5 3 3 3 5 3 2 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 5 3 3 5 3 3 3.0 5 3 2
25 26 24 1 3 4 3 1 1 4 2 3 5 2 2 3 1 1 3 4 5 4 4 4 3 3 3 3 3 4 5 5 5 5 3 3 3 3 3 3 5 4 4 5 5 3 5 5 5 3 5 2 1 1 3 1 2 2 1 5 1 5 5 5 5 5 5 5 2 2 2 5 2 2 5 5 5 5.0 3 3 3
26 27 24 1 2 2 2 1 1 1 1 3 1 2 2 2 1 1 2 4 2 5 4 3 3 5 5 3 4 5 5 4 4 4 4 3 3 3 5 5 4 4 3 3 4 3 3 4 4 4 1 1 1 4 5 1 1 1 1 1 1 1 5 1 1 1 1 1 4 4 2 5 3 5 5 4 5 5.0 4 4 4
27 28 24 1 3 5 2 2 1 1 1 2 2 2 2 1 1 2 1 1 2 5 3 3 3 5 5 3 4 5 5 4 4 4 4 3 3 3 5 5 4 4 3 3 4 3 3 4 4 4 1 1 1 1 1 1 1 1 1 1 1 1 3 1 1 1 1 1 4 1 1 5 3 5 5 4 5 4.0 4 3 3
28 29 24 1 2 2 2 1 1 1 1 2 3 2 2 1 1 2 2 1 2 4 4 3 3 5 5 3 4 5 5 4 4 4 3 3 3 3 5 5 4 3 3 3 4 3 3 4 4 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 2 3 5 3 4 5 4 3 5.0 4 4 3
29 30 23 1 3 2 2 1 1 3 4 2 3 1 1 1 1 1 1 1 3 4 4 3 2 5 5 2 5 4 4 3 4 3 2 2 1 3 5 3 1 1 1 1 1 1 1 1 3 1 1 1 1 1 5 1 1 1 1 1 1 1 3 3 3 3 3 3 3 3 4 5 1 2 5 3 5 5.0 2 1 1
30 31 22 1 3 5 2 2 2 5 2 4 2 1 2 1 1 1 1 1 4 5 3 2 3 5 5 3 5 5 5 5 5 5 5 2 2 3 5 5 5 5 5 5 5 5 5 5 4 5 2 1 1 3 5 1 3 1 1 1 1 5 5 5 1 1 1 1 4 3 3 4 3 4 5 5 5 5.0 3 5 4
31 32 22 1 3 2 2 2 1 1 3 2 4 1 1 1 1 2 1 4 4 4 4 4 4 4 4 4 4 5 4 4 4 3 4 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 1 1 1 3 1 1 3 3 4 4.0 3 3 2
32 33 22 1 3 1 3 1 1 1 1 2 2 1 1 1 1 2 1 3 2 5 5 2 2 4 4 3 5 5 5 3 5 5 5 3 3 3 3 3 4 4 5 4 4 2 4 4 5 5 5 1 1 4 5 1 1 1 1 1 1 5 5 1 1 1 5 5 4 3 3 5 2 3 5 3 4 5.0 3 4 1
33 34 22 1 3 2 2 1 1 5 4 2 3 1 1 1 2 2 1 5 3 4 4 4 4 4 4 4 4 5 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 4 3 1 1 1 1 1 1 1 2 4 2 2 2 2 2 5 3 4 3 5 5 5 5 5 5.0 5 5 5
34 35 24 1 3 2 1 1 2 1 2 1 3 1 2 2 2 1 1 2 2 5 3 3 3 3 3 3 3 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 2 1 1 1 2 3 2 3 1 3 1 3 3 1 3 5 3 3 3 3 3 3 4 3 4 3 5 3 5.0 4 3 3
35 36 24 1 3 2 1 1 1 3 2 2 5 2 1 1 2 2 2 1 5 5 5 4 3 5 3 4 1 5 4 3 5 4 3 5 4 3 5 5 4 3 5 3 5 3 5 4 3 5 1 4 3 2 1 2 3 4 5 5 5 2 2 2 3 2 2 2 5 4 3 4 5 3 4 5 4 3.0 5 4 3
36 37 24 1 3 2 3 1 1 3 3 3 4 3 2 3 1 1 4 1 2 4 1 2 3 3 2 4 3 4 2 3 1 4 2 3 1 2 3 1 4 3 2 4 2 5 2 4 5 3 3 4 2 3 2 5 2 3 2 2 2 1 3 3 4 3 6 4 4 3 4 2 3 4 3 5 3 5.0 2 4 2
37 38 21 1 4 2 1 1 1 1 2 2 2 4 4 4 2 2 3 2 2 4 4 4 4 5 5 4 3 5 4 3 3 4 4 4 4 3 4 4 3 3 3 4 4 4 3 4 5 2 5 5 1 1 1 1 1 1 1 5 1 2 7 3 4 6 4 4 4 4 5 5 4 3 5 5 3 4.0 5 4 5
38 39 24 1 3 2 2 2 1 4 4 2 3 3 3 4 2 1 4 4 4 5 5 4 4 3 3 2 2 5 4 3 5 5 5 4 4 3 3 4 4 5 5 4 4 5 4 3 4 5 3 4 5 5 5 5 3 1 2 3 4 1 2 3 4 1 7 7 4 4 4 3 2 1 1 1 1 2.0 3 4 5
39 40 25 1 3 4 3 1 2 1 2 1 3 1 1 1 1 1 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 4 4 4 2 2 1 3 3 3 4 4 5 5 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 3 3 3 3 3 3 3 3 3 3.0 3 3 3
40 41 24 1 3 2 2 1 1 4 2 4 3 4 4 4 1 1 4 7 3 1 2 2 2 3 2 1 2 1 2 3 3 3 2 2 1 4 4 2 3 3 5 2 2 2 1 2 2 4 5 5 5 5 5 5 5 5 5 5 5 1 1 1 1 1 1 1 3 3 3 1 1 5 1 4 4 4.0 2 4 2
41 42 23 1 3 2 1 1 1 3 2 3 2 1 2 2 2 1 2 2 3 4 1 3 3 3 3 1 3 5 3 2 3 2 2 3 3 3 2 4 2 2 3 2 1 4 3 1 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 3 3 3 3 3 3 3 3 3.0 3 3 3
42 43 25 1 3 2 2 1 2 2 3 1 1 1 1 1 2 1 1 3 2 5 5 5 5 5 5 5 5 5 4 5 4 5 4 5 4 5 4 5 4 2 3 4 5 4 3 4 5 4 2 3 2 3 2 1 2 5 5 5 5 1 2 3 4 5 6 7 2 2 1 1 2 3 4 5 3 2.0 3 2 1
43 44 28 1 2 2 3 1 2 1 2 2 5 2 2 2 1 1 1 2 3 5 3 3 3 3 3 3 3 3 4 4 3 3 3 3 3 3 3 3 3 3 2 3 2 3 4 3 2 3 5 3 1 1 1 1 1 1 1 1 1 3 5 5 3 3 3 3 3 3 3 3 3 3 3 3 3 3.0 3 3 3
44 45 22 1 3 6 2 1 1 1 1 3 3 2 2 1 1 1 4 4 1 4 4 4 3 1 2 2 5 5 5 4 4 4 4 5 3 4 1 3 5 3 2 4 4 3 2 3 4 2 2 1 1 1 2 2 5 1 1 1 1 1 5 1 1 1 1 1 2 1 2 5 1 3 5 5 4 5.0 2 3 3
45 46 24 1 3 2 3 1 1 2 3 3 4 1 2 3 2 1 1 2 3 3 3 3 3 3 3 3 5 4 4 4 5 3 5 3 3 3 4 2 5 5 3 3 3 3 2 4 4 4 4 5 1 4 4 4 3 1 1 1 1 3 2 2 1 1 1 1 2 2 2 5 2 3 5 4 4 5.0 5 4 4
46 47 24 1 3 2 2 2 2 3 1 2 1 2 2 2 1 1 1 6 5 4 3 4 4 3 2 5 4 5 3 3 3 4 2 1 4 4 5 5 5 2 2 2 4 4 1 3 3 3 1 3 3 2 2 4 1 1 1 1 1 2 2 1 1 1 1 1 4 3 5 4 4 2 5 2 1 3.0 5 2 4
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519 520 22 1 3 4 2 2 1 1 1 2 2 1 1 2 1 2 1 3 2 3 4 4 4 4 4 3 4 5 4 4 4 4 4 4 4 4 4 4 2 2 2 2 5 5 2 3 4 4 5 5 3 1 3 1 3 1 1 1 1 3 3 3 3 3 3 3 4 3 3 5 4 4 5 5 3 5.0 5 5 5
520 521 18 0 2 4 2 2 1 2 2 1 3 1 1 1 1 2 1 5 3 4 3 3 3 4 4 3 4 4 3 4 4 4 3 3 3 3 4 4 4 2 2 2 2 4 2 3 3 4 5 5 1 4 4 4 4 4 1 4 4 6 6 6 6 6 6 6 4 3 3 5 2 5 5 3 3 5.0 5 5 5
521 522 21 1 3 2 1 2 1 1 1 2 3 1 1 1 1 2 1 4 3 4 4 3 3 4 4 3 4 4 4 4 4 4 3 3 3 3 4 4 3 2 2 2 4 4 2 3 4 4 5 5 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 3 3 3 5 4 5 5 3 4 5.0 5 5 5
522 523 20 1 2 4 2 1 1 3 2 2 3 1 1 2 1 2 1 1 3 4 4 3 3 4 4 3 4 4 4 4 4 4 4 3 3 3 4 4 3 2 2 2 5 5 2 3 4 5 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 3 3 3 5 4 4 5 4 4 5.0 5 5 5
523 524 23 1 3 4 3 2 1 3 1 2 3 1 1 1 1 2 1 1 3 5 5 5 4 4 5 3 4 5 4 4 4 4 3 5 3 3 3 5 4 2 2 2 5 5 2 3 4 4 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 3 3 2 5 4 4 5 3 3 5.0 5 5 5
524 525 23 0 3 4 2 1 1 1 1 2 2 1 1 1 1 2 1 2 2 4 4 3 3 3 3 3 4 4 4 4 4 4 4 3 3 3 3 3 4 2 2 2 2 4 1 3 4 4 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 3 2 2 1 5 3 3 5 3 3 5.0 5 5 5
525 526 22 0 3 4 2 1 2 5 4 2 1 1 1 2 1 1 1 2 1 3 4 3 3 4 4 3 3 4 4 4 4 4 4 4 4 3 4 4 3 2 2 2 3 3 3 3 3 3 5 1 1 1 1 1 1 2 2 2 2 3 3 3 1 3 3 4 3 3 3 5 3 3 5 3 3 5.0 5 5 5
526 527 22 0 3 4 4 1 1 5 1 4 3 1 1 2 1 2 1 1 3 5 5 5 4 5 4 4 5 5 5 5 5 5 4 4 4 4 5 5 4 4 4 4 5 5 4 4 4 5 5 5 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 3 3 3 5 3 4 5 4 5 5.0 5 5 5
In [8]:
qstns = pd.read_csv('Questionnaire.csv', encoding='latin-1')
#qstns.columns=None
qstns
Out[8]:
Question_ID Feature Question
0 Q1 Record_ID Record_ID
1 Q2 Age Age
2 Q3 Gender Gender (Male/Female)
3 Q4 Education Educational Qualification - 1) Intermediate/eq...
4 Q5 Father Occupation Father occupation - 1) Coolie 2)Farmer 3)Self-...
5 Q6 Household Income Household income per month - 1)<Rs.5000 2)R...
6 Q7 Native (Rural/Urban) Where are you from?(Rural/Urban)
7 Q8 Handset Type(Color/ Black & white) Type of your handset?(Color/ Black & white)
8 Q9 Handset Brand Which brand of handset are you using currently...
9 Q10 Handset Selection Reason What made you to select this handset? - 1)Bran...
10 Q11 Handset Cost How much price does your handset costs ? - 1)<...
11 Q12 Handset Age Since how long you have been using this handse...
12 Q13 Calls Receiving per day How many calls you receive per a day? - 1)<10 ...
13 Q14 SMS Receiving per day How many SMS you receive per a day? - 1)<10 2)...
14 Q15 SMS Sending per day How many SMS you send per a day? - 1)<10 2)10-...
15 Q16 Frequent Communication Type Which form of communication do you usually us...
16 Q17 Feel safe and secure with mobile Do you face any problems when you lose/ do not...
17 Q18 Monthly Mobile Expenditure How much you are spending towards mobile per m...
18 Q19 Current Service Provider Which service provider do you use currently? -...
19 Q20 Current Service Provider Age How long you have been using this service prov...
20 Q21-1 SP - Influence Rating for Signal Rate the influence of the following factors in...
21 Q21-2 SP - Influence Rating for Customer care Rate the influence of the following factors in...
22 Q21-3 SP - Influence Rating for Electronic media Rate the influence of the following factors in...
23 Q21-4 SP - Influence Rating for Hoardings Rate the influence of the following factors in...
24 Q21-5 SP - Influence Rating for Friends Rate the influence of the following factors in...
25 Q21-6 SP - Influence Rating for Family members Rate the influence of the following factors in...
26 Q21-7 SP - Influence Rating for Print media Rate the influence of the following factors in...
27 Q21-8 SP - Influence Rating for Call costs Rate the influence of the following factors in...
28 Q22-1 HS - Influence Rating for Brand Rate the influence of the following factors in...
29 Q22-2 HS - Influence Rating for Battery back up Rate the influence of the following factors in...
30 Q22-3 HS - Influence Rating for Keypad Rate the influence of the following factors in...
31 Q22-4 HS - Influence Rating for Display Rate the influence of the following factors in...
32 Q22-5 HS - Influence Rating for Sound Rate the influence of the following factors in...
33 Q22-6 HS - Influence Rating for Size of the screen Rate the influence of the following factors in...
34 Q22-7 HS - Influence Rating for Electronic media Rate the influence of the following factors in...
35 Q22-8 HS - Influence Rating for Print media Rate the influence of the following factors in...
36 Q22-9 HS - Influence Rating for Hoardings Rate the influence of the following factors in...
37 Q22-10 HS - Influence Rating for Friends Rate the influence of the following factors in...
38 Q22-11 HS - Influence Rating for Family members Rate the influence of the following factors in...
39 Q22-12 HS - Influence Rating for Voice clarity Rate the influence of the following factors in...
40 Q22-13 HS - Influence Rating for MP3 facility Rate the influence of the following factors in...
41 Q22-14 HS - Influence Rating for Bluetooth Rate the influence of the following factors in...
42 Q22-15 HS - Influence Rating for Camera pixels Rate the influence of the following factors in...
43 Q22-16 HS - Influence Rating for FM radio Rate the influence of the following factors in...
44 Q22-17 HS - Influence Rating for Games Rate the influence of the following factors in...
45 Q22-18 HS - Influence Rating for Video Rate the influence of the following factors in...
46 Q22-19 HS - Influence Rating for Memory size Rate the influence of the following factors in...
47 Q22-20 HS - Influence Rating for Price Rate the influence of the following factors in...
48 Q22-21 HS - Influence Rating for Stylish Rate the influence of the following factors in...
49 Q23-1 Stylish Handset Brand In your opinion which of the following feature...
50 Q23-2 More Models Handset Brand In your opinion which of the following feature...
51 Q23-3 User friendly Handset Brand In your opinion which of the following feature...
52 Q23-4 Reasonable prices Handset Brand In your opinion which of the following feature...
53 Q23-5 Added features Handset Brand In your opinion which of the following feature...
54 Q23-6 Customer service Handset Brand In your opinion which of the following feature...
55 Q23-7 Advertisements Handset Brand In your opinion which of the following feature...
56 Q23-8 Sales promotions Handset Brand In your opinion which of the following feature...
57 Q23-9 Available service centers Handset Brand In your opinion which of the following feature...
58 Q23-10 Durability Handset Brand In your opinion which of the following feature...
59 Q23-11 Overall, I like the Handset Brand In your opinion which of the following feature...
60 Q24-1 User friendly Service Provider In your opinion which of the following feature...
61 Q24-2 Added features Service Provider In your opinion which of the following feature...
62 Q24-3 Customer service Service Provider In your opinion which of the following feature...
63 Q24-4 Advertisements Service Provider In your opinion which of the following feature...
64 Q24-5 Sales promotions Service Provider In your opinion which of the following feature...
65 Q24-6 Available service centers Service Provider In your opinion which of the following feature...
66 Q24-7 Overall, I like Service Provider In your opinion which of the following feature...
67 Q25-1 All my friends use I use cell phone because (Rate): - I use cell ...
68 Q25-2 Old fashioned I use cell phone because (Rate): - I look old ...
69 Q25-3 Prestige I use cell phone because (Rate): - It brings m...
70 Q25-4 Secure I use cell phone because (Rate): - I feel secu...
71 Q25-5 Show-off I use cell phone because (Rate): - I like show...
72 Q25-6 Life style I use cell phone because (Rate): - It matches ...
73 Q25-7 Communication I use cell phone because (Rate): - I need it f...
74 Q25-8 Facilities I use cell phone because (Rate): - I make use ...
75 Q25-9 Life easier I use cell phone because (Rate): - It makes m...
76 Q25-10 Call Friends and Family I use cell phone because (Rate): - Mostly use ...
77 Q25-11 Mostly use SMS I use cell phone because (Rate): - Mostly use ...
78 Q25-12 Music or Videos I use cell phone because (Rate): - Mostly use ...
79 Q25-13 Games I use cell phone because (Rate): - Mostly use ...
In [9]:
qstns.iloc[:, 1]
Out[9]:
0                                        Record_ID
1                                              Age
2                                           Gender
3                                        Education
4                                Father Occupation
5                                 Household Income
6                             Native (Rural/Urban)
7               Handset Type(Color/ Black & white)
8                                    Handset Brand
9                         Handset Selection Reason
10                                    Handset Cost
11                                     Handset Age
12                         Calls Receiving per day
13                           SMS Receiving per day
14                             SMS Sending per day
15                     Frequent Communication Type
16                Feel safe and secure with mobile
17                      Monthly Mobile Expenditure
18                        Current Service Provider
19                    Current Service Provider Age
20                SP - Influence Rating for Signal
21         SP - Influence Rating for Customer care
22      SP - Influence Rating for Electronic media
23             SP - Influence Rating for Hoardings
24               SP - Influence Rating for Friends
25        SP - Influence Rating for Family members
26           SP - Influence Rating for Print media
27            SP - Influence Rating for Call costs
28                 HS - Influence Rating for Brand
29       HS - Influence Rating for Battery back up
30                HS - Influence Rating for Keypad
31               HS - Influence Rating for Display
32                 HS - Influence Rating for Sound
33    HS - Influence Rating for Size of the screen
34      HS - Influence Rating for Electronic media
35           HS - Influence Rating for Print media
36             HS - Influence Rating for Hoardings
37               HS - Influence Rating for Friends
38        HS - Influence Rating for Family members
39         HS - Influence Rating for Voice clarity
40          HS - Influence Rating for MP3 facility
41             HS - Influence Rating for Bluetooth
42         HS - Influence Rating for Camera pixels
43              HS - Influence Rating for FM radio
44                 HS - Influence Rating for Games
45                 HS - Influence Rating for Video
46           HS - Influence Rating for Memory size
47                 HS - Influence Rating for Price
48               HS - Influence Rating for Stylish
49                           Stylish Handset Brand
50                       More Models Handset Brand
51                     User friendly Handset Brand
52                 Reasonable prices Handset Brand
53                    Added features Handset Brand
54                  Customer service Handset Brand
55                    Advertisements Handset Brand
56                  Sales promotions Handset Brand
57         Available service centers Handset Brand
58                        Durability Handset Brand
59               Overall, I like the Handset Brand
60                  User friendly Service Provider
61                 Added features Service Provider
62               Customer service Service Provider
63                 Advertisements Service Provider
64               Sales promotions Service Provider
65      Available service centers Service Provider
66                Overall, I like Service Provider
67                              All my friends use
68                                   Old fashioned
69                                        Prestige
70                                          Secure
71                                        Show-off
72                                      Life style
73                                   Communication
74                                      Facilities
75                                     Life easier
76                         Call Friends and Family
77                                  Mostly use SMS
78                                 Music or Videos
79                                           Games
Name: Feature, dtype: object
In [10]:
qstns.iloc[:, 2]
Out[10]:
0                                             Record_ID
1                                                   Age
2                                  Gender (Male/Female)
3     Educational Qualification - 1) Intermediate/eq...
4     Father occupation - 1) Coolie 2)Farmer 3)Self-...
5     Household income per month - 1)<Rs.5000    2)R...
6                      Where are you from?(Rural/Urban)
7           Type of your handset?(Color/ Black & white)
8     Which brand of handset are you using currently...
9     What made you to select this handset? - 1)Bran...
10    How much price does your handset costs ? - 1)<...
11    Since how long you have been using this handse...
12    How many calls you receive per a day? - 1)<10 ...
13    How many SMS you receive per a day? - 1)<10 2)...
14    How many SMS you send per a day? - 1)<10 2)10-...
15    Which form of  communication do you usually us...
16    Do you face any problems when you lose/ do not...
17    How much you are spending towards mobile per m...
18    Which service provider do you use currently? -...
19    How long you have been using this service prov...
20    Rate the influence of the following factors in...
21    Rate the influence of the following factors in...
22    Rate the influence of the following factors in...
23    Rate the influence of the following factors in...
24    Rate the influence of the following factors in...
25    Rate the influence of the following factors in...
26    Rate the influence of the following factors in...
27    Rate the influence of the following factors in...
28    Rate the influence of the following factors in...
29    Rate the influence of the following factors in...
30    Rate the influence of the following factors in...
31    Rate the influence of the following factors in...
32    Rate the influence of the following factors in...
33    Rate the influence of the following factors in...
34    Rate the influence of the following factors in...
35    Rate the influence of the following factors in...
36    Rate the influence of the following factors in...
37    Rate the influence of the following factors in...
38    Rate the influence of the following factors in...
39    Rate the influence of the following factors in...
40    Rate the influence of the following factors in...
41    Rate the influence of the following factors in...
42    Rate the influence of the following factors in...
43    Rate the influence of the following factors in...
44    Rate the influence of the following factors in...
45    Rate the influence of the following factors in...
46    Rate the influence of the following factors in...
47    Rate the influence of the following factors in...
48    Rate the influence of the following factors in...
49    In your opinion which of the following feature...
50    In your opinion which of the following feature...
51    In your opinion which of the following feature...
52    In your opinion which of the following feature...
53    In your opinion which of the following feature...
54    In your opinion which of the following feature...
55    In your opinion which of the following feature...
56    In your opinion which of the following feature...
57    In your opinion which of the following feature...
58    In your opinion which of the following feature...
59    In your opinion which of the following feature...
60    In your opinion which of the following feature...
61    In your opinion which of the following feature...
62    In your opinion which of the following feature...
63    In your opinion which of the following feature...
64    In your opinion which of the following feature...
65    In your opinion which of the following feature...
66    In your opinion which of the following feature...
67    I use cell phone because (Rate): - I use cell ...
68    I use cell phone because (Rate): - I look old ...
69    I use cell phone because (Rate): - It brings m...
70    I use cell phone because (Rate): - I feel secu...
71    I use cell phone because (Rate): - I like show...
72    I use cell phone because (Rate): - It matches ...
73    I use cell phone because (Rate): - I need it f...
74    I use cell phone because (Rate): - I make use ...
75    I use cell phone because (Rate): - It  makes m...
76    I use cell phone because (Rate): - Mostly use ...
77    I use cell phone because (Rate): - Mostly use ...
78    I use cell phone because (Rate): - Mostly use ...
79    I use cell phone because (Rate): - Mostly use ...
Name: Question, dtype: object
In [11]:
qstns['Feature']
Out[11]:
0                                        Record_ID
1                                              Age
2                                           Gender
3                                        Education
4                                Father Occupation
5                                 Household Income
6                             Native (Rural/Urban)
7               Handset Type(Color/ Black & white)
8                                    Handset Brand
9                         Handset Selection Reason
10                                    Handset Cost
11                                     Handset Age
12                         Calls Receiving per day
13                           SMS Receiving per day
14                             SMS Sending per day
15                     Frequent Communication Type
16                Feel safe and secure with mobile
17                      Monthly Mobile Expenditure
18                        Current Service Provider
19                    Current Service Provider Age
20                SP - Influence Rating for Signal
21         SP - Influence Rating for Customer care
22      SP - Influence Rating for Electronic media
23             SP - Influence Rating for Hoardings
24               SP - Influence Rating for Friends
25        SP - Influence Rating for Family members
26           SP - Influence Rating for Print media
27            SP - Influence Rating for Call costs
28                 HS - Influence Rating for Brand
29       HS - Influence Rating for Battery back up
30                HS - Influence Rating for Keypad
31               HS - Influence Rating for Display
32                 HS - Influence Rating for Sound
33    HS - Influence Rating for Size of the screen
34      HS - Influence Rating for Electronic media
35           HS - Influence Rating for Print media
36             HS - Influence Rating for Hoardings
37               HS - Influence Rating for Friends
38        HS - Influence Rating for Family members
39         HS - Influence Rating for Voice clarity
40          HS - Influence Rating for MP3 facility
41             HS - Influence Rating for Bluetooth
42         HS - Influence Rating for Camera pixels
43              HS - Influence Rating for FM radio
44                 HS - Influence Rating for Games
45                 HS - Influence Rating for Video
46           HS - Influence Rating for Memory size
47                 HS - Influence Rating for Price
48               HS - Influence Rating for Stylish
49                           Stylish Handset Brand
50                       More Models Handset Brand
51                     User friendly Handset Brand
52                 Reasonable prices Handset Brand
53                    Added features Handset Brand
54                  Customer service Handset Brand
55                    Advertisements Handset Brand
56                  Sales promotions Handset Brand
57         Available service centers Handset Brand
58                        Durability Handset Brand
59               Overall, I like the Handset Brand
60                  User friendly Service Provider
61                 Added features Service Provider
62               Customer service Service Provider
63                 Advertisements Service Provider
64               Sales promotions Service Provider
65      Available service centers Service Provider
66                Overall, I like Service Provider
67                              All my friends use
68                                   Old fashioned
69                                        Prestige
70                                          Secure
71                                        Show-off
72                                      Life style
73                                   Communication
74                                      Facilities
75                                     Life easier
76                         Call Friends and Family
77                                  Mostly use SMS
78                                 Music or Videos
79                                           Games
Name: Feature, dtype: object
In [12]:
qstns['Question']
Out[12]:
0                                             Record_ID
1                                                   Age
2                                  Gender (Male/Female)
3     Educational Qualification - 1) Intermediate/eq...
4     Father occupation - 1) Coolie 2)Farmer 3)Self-...
5     Household income per month - 1)<Rs.5000    2)R...
6                      Where are you from?(Rural/Urban)
7           Type of your handset?(Color/ Black & white)
8     Which brand of handset are you using currently...
9     What made you to select this handset? - 1)Bran...
10    How much price does your handset costs ? - 1)<...
11    Since how long you have been using this handse...
12    How many calls you receive per a day? - 1)<10 ...
13    How many SMS you receive per a day? - 1)<10 2)...
14    How many SMS you send per a day? - 1)<10 2)10-...
15    Which form of  communication do you usually us...
16    Do you face any problems when you lose/ do not...
17    How much you are spending towards mobile per m...
18    Which service provider do you use currently? -...
19    How long you have been using this service prov...
20    Rate the influence of the following factors in...
21    Rate the influence of the following factors in...
22    Rate the influence of the following factors in...
23    Rate the influence of the following factors in...
24    Rate the influence of the following factors in...
25    Rate the influence of the following factors in...
26    Rate the influence of the following factors in...
27    Rate the influence of the following factors in...
28    Rate the influence of the following factors in...
29    Rate the influence of the following factors in...
30    Rate the influence of the following factors in...
31    Rate the influence of the following factors in...
32    Rate the influence of the following factors in...
33    Rate the influence of the following factors in...
34    Rate the influence of the following factors in...
35    Rate the influence of the following factors in...
36    Rate the influence of the following factors in...
37    Rate the influence of the following factors in...
38    Rate the influence of the following factors in...
39    Rate the influence of the following factors in...
40    Rate the influence of the following factors in...
41    Rate the influence of the following factors in...
42    Rate the influence of the following factors in...
43    Rate the influence of the following factors in...
44    Rate the influence of the following factors in...
45    Rate the influence of the following factors in...
46    Rate the influence of the following factors in...
47    Rate the influence of the following factors in...
48    Rate the influence of the following factors in...
49    In your opinion which of the following feature...
50    In your opinion which of the following feature...
51    In your opinion which of the following feature...
52    In your opinion which of the following feature...
53    In your opinion which of the following feature...
54    In your opinion which of the following feature...
55    In your opinion which of the following feature...
56    In your opinion which of the following feature...
57    In your opinion which of the following feature...
58    In your opinion which of the following feature...
59    In your opinion which of the following feature...
60    In your opinion which of the following feature...
61    In your opinion which of the following feature...
62    In your opinion which of the following feature...
63    In your opinion which of the following feature...
64    In your opinion which of the following feature...
65    In your opinion which of the following feature...
66    In your opinion which of the following feature...
67    I use cell phone because (Rate): - I use cell ...
68    I use cell phone because (Rate): - I look old ...
69    I use cell phone because (Rate): - It brings m...
70    I use cell phone because (Rate): - I feel secu...
71    I use cell phone because (Rate): - I like show...
72    I use cell phone because (Rate): - It matches ...
73    I use cell phone because (Rate): - I need it f...
74    I use cell phone because (Rate): - I make use ...
75    I use cell phone because (Rate): - It  makes m...
76    I use cell phone because (Rate): - Mostly use ...
77    I use cell phone because (Rate): - Mostly use ...
78    I use cell phone because (Rate): - Mostly use ...
79    I use cell phone because (Rate): - Mostly use ...
Name: Question, dtype: object
In [13]:
data.columns
Out[13]:
Index(['Q1', 'Q2', 'Q3', 'Q4', 'Q5', 'Q6', 'Q7', 'Q8', 'Q9', 'Q10', 'Q11',
       'Q12', 'Q13', 'Q14', 'Q15', 'Q16', 'Q17', 'Q18', 'Q19', 'Q20', 'Q21-1',
       'Q21-2', 'Q21-3', 'Q21-4', 'Q21-5', 'Q21-6', 'Q21-7', 'Q21-8', 'Q22-1',
       'Q22-2', 'Q22-3', 'Q22-4', 'Q22-5', 'Q22-6', 'Q22-7', 'Q22-8', 'Q22-9',
       'Q22-10', 'Q22-11', 'Q22-12', 'Q22-13', 'Q22-14', 'Q22-15', 'Q22-16',
       'Q22-17', 'Q22-18', 'Q22-19', 'Q22-20', 'Q22-21', 'Q23-1', 'Q23-2',
       'Q23-3', 'Q23-4', 'Q23-5', 'Q23-6', 'Q23-7', 'Q23-8', 'Q23-9', 'Q23-10',
       'Q23-11', 'Q24-1', 'Q24-2', 'Q24-3', 'Q24-4', 'Q24-5', 'Q24-6', 'Q24-7',
       'Q25-1', 'Q25-2', 'Q25-3', 'Q25-4', 'Q25-5', 'Q25-6', 'Q25-7', 'Q25-8',
       'Q25-9', 'Q25-10', 'Q25-11', 'Q25-12', 'Q25-13'],
      dtype='object')
In [14]:
data1 = data.copy()  # Create a copy of the original DataFrame
data1.columns = qstns['Feature']
data1.index.name = None
data1
Out[14]:
Feature Record_ID Age Gender Education Father Occupation Household Income Native (Rural/Urban) Handset Type(Color/ Black & white) Handset Brand Handset Selection Reason Handset Cost Handset Age Calls Receiving per day SMS Receiving per day SMS Sending per day Frequent Communication Type Feel safe and secure with mobile Monthly Mobile Expenditure Current Service Provider Current Service Provider Age SP - Influence Rating for Signal SP - Influence Rating for Customer care SP - Influence Rating for Electronic media SP - Influence Rating for Hoardings SP - Influence Rating for Friends SP - Influence Rating for Family members SP - Influence Rating for Print media SP - Influence Rating for Call costs HS - Influence Rating for Brand HS - Influence Rating for Battery back up HS - Influence Rating for Keypad HS - Influence Rating for Display HS - Influence Rating for Sound HS - Influence Rating for Size of the screen HS - Influence Rating for Electronic media HS - Influence Rating for Print media HS - Influence Rating for Hoardings HS - Influence Rating for Friends HS - Influence Rating for Family members HS - Influence Rating for Voice clarity HS - Influence Rating for MP3 facility HS - Influence Rating for Bluetooth HS - Influence Rating for Camera pixels HS - Influence Rating for FM radio HS - Influence Rating for Games HS - Influence Rating for Video HS - Influence Rating for Memory size HS - Influence Rating for Price HS - Influence Rating for Stylish Stylish Handset Brand More Models Handset Brand User friendly Handset Brand Reasonable prices Handset Brand Added features Handset Brand Customer service Handset Brand Advertisements Handset Brand Sales promotions Handset Brand Available service centers Handset Brand Durability Handset Brand Overall, I like the Handset Brand User friendly Service Provider Added features Service Provider Customer service Service Provider Advertisements Service Provider Sales promotions Service Provider Available service centers Service Provider Overall, I like Service Provider All my friends use Old fashioned Prestige Secure Show-off Life style Communication Facilities Life easier Call Friends and Family Mostly use SMS Music or Videos Games
0 1 21 1 2 2 2 1 1 2 2 2 2 1 2 2 1 1 1 5 4 4 4 3 2 5 4 2 5 5 4 4 4 3 3 3 2 2 4 4 5 3 3 3 4 4 2 4 5 4 1 1 4 3 1 1 1 1 1 1 1 1 1 1 4 5 1 1 3 2 2 4 3 4 4 4 4 4.0 2 3 2
1 2 24 1 3 2 1 1 1 3 2 3 2 1 1 1 2 1 1 3 2 4 5 4 3 4 4 3 3 3 3 5 4 4 5 4 4 4 5 3 3 1 1 1 5 3 1 2 4 5 3 5 1 4 5 1 4 1 1 1 1 3 7 5 1 1 1 3 5 1 3 5 2 5 5 3 4 5.0 3 2 3
2 3 21 1 3 2 2 1 1 1 1 3 2 2 2 1 1 2 2 1 2 5 4 3 3 5 5 3 4 5 5 4 4 4 4 3 3 3 5 5 4 4 3 3 4 3 3 4 4 4 1 1 1 4 5 1 1 1 1 1 1 2 5 1 1 1 1 1 4 2 2 5 2 2 5 4 4 5.0 4 4 3
3 4 21 1 3 2 1 1 1 1 1 2 3 1 1 1 2 2 1 1 3 5 4 4 4 4 4 4 4 4 4 4 4 3 4 4 3 4 4 4 4 4 4 3 4 4 4 4 4 4 4 4 4 4 4 1 1 1 1 1 1 4 4 4 4 4 4 4 4 4 3 4 4 2 4 4 4 4.0 4 5 4
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474 475 22 1 3 2 3 1 1 5 2 3 1 2 3 3 2 1 1 2 1 4 4 3 4 5 5 3 5 5 5 5 5 5 5 5 5 5 5 4 5 5 5 5 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3 3 3 3 3 1 1 2 5 3 5 5 4 4 5.0 5 5 3
475 476 22 1 3 1 2 1 1 1 2 2 3 1 4 4 2 1 1 1 3 2 5 5 4 5 4 3 1 5 4 3 5 4 4 4 1 1 4 4 5 5 5 4 4 3 4 3 2 3 5 5 1 1 1 1 5 2 1 2 3 7 7 1 1 1 5 7 4 3 2 5 4 4 5 5 4 4.0 5 5 4
476 477 20 1 3 2 1 1 1 5 2 3 2 1 1 1 2 2 1 1 2 5 5 5 5 4 4 4 4 5 5 4 3 4 4 4 4 4 4 4 4 1 1 4 4 4 4 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 2 2 4 2 2 5 4 2 4.0 5 3 2
477 478 22 1 3 5 4 1 1 1 4 3 5 3 3 3 1 1 4 2 3 5 2 4 3 5 5 4 5 4 5 3 4 4 5 5 2 3 4 4 4 5 4 5 3 4 4 5 4 5 1 3 1 5 1 1 3 1 1 1 1 3 3 4 3 4 3 3 2 2 2 5 2 4 4 3 2 4.0 4 2 2
478 479 22 1 3 2 2 1 1 2 2 2 3 1 1 1 1 1 1 1 3 5 5 5 5 5 5 5 5 5 2 5 2 5 2 5 2 5 5 5 2 2 2 2 2 2 2 2 5 5 4 5 1 1 1 5 5 1 1 1 1 1 1 1 1 1 1 1 5 2 2 5 2 2 5 5 5 5.0 2 2 2
479 480 22 1 3 5 2 1 2 1 2 2 3 1 1 1 1 1 1 1 3 5 4 3 3 5 4 3 4 4 5 4 4 5 4 3 3 3 5 5 4 1 1 1 4 3 1 4 4 4 3 3 4 2 5 1 1 4 1 5 1 5 6 5 5 6 4 5 1 2 1 4 2 1 5 3 2 4.0 2 1 1
480 481 25 1 3 1 1 1 2 1 1 1 1 1 1 1 1 3 1 1 1 5 4 3 2 3 5 2 4 4 5 3 3 3 2 2 2 2 3 5 3 4 3 3 3 3 3 2 3 3 1 1 5 1 5 1 3 1 5 1 1 3 3 1 3 3 3 3 3 3 3 5 3 3 5 3 3 4.0 3 3 3
481 482 22 1 3 2 1 1 1 5 2 2 3 2 2 1 1 1 1 1 3 5 5 5 5 5 5 4 3 5 5 5 5 5 5 5 4 4 5 5 5 2 2 2 5 4 2 2 4 4 3 5 1 1 2 1 4 1 2 3 1 1 1 1 1 1 1 1 5 4 4 5 4 5 4 5 4 5.0 4 4 3
482 483 22 1 3 6 1 1 1 3 2 2 1 1 2 2 2 2 4 2 1 4 3 2 3 3 4 4 4 4 4 4 5 5 5 5 5 4 4 4 5 5 1 1 5 4 1 4 5 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 4 3 4 4 4 4 4.0 5 4 4
483 484 24 1 3 2 2 1 2 4 2 2 2 2 2 3 1 2 2 3 2 5 3 2 2 3 5 2 3 4 4 3 3 4 3 2 2 2 3 5 3 3 2 2 3 2 3 2 3 3 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 3 3 3 4 3 2 5 3 3 3.0 3 3 3
484 485 22 1 3 2 3 1 1 1 3 2 1 1 1 1 2 2 1 4 1 5 5 3 3 5 5 3 5 4 5 4 4 4 4 3 3 3 4 4 4 3 3 3 4 3 3 3 4 4 5 5 1 1 3 1 5 1 1 1 1 2 1 5 4 5 5 3 2 1 1 4 1 2 4 3 3 5.0 4 3 2
485 486 28 1 3 2 1 1 2 1 2 2 2 1 1 1 1 2 1 3 2 5 4 3 3 3 5 3 4 4 5 4 4 4 3 3 3 3 3 5 5 3 3 2 4 3 4 3 4 4 1 2 1 3 1 2 3 3 2 1 1 4 4 4 4 4 4 4 4 4 4 5 4 3 5 3 3 5.0 3 5 4
486 487 23 1 3 2 2 1 1 3 2 2 3 1 1 1 1 2 1 5 1 1 4 3 4 5 4 3 3 4 5 3 5 4 5 5 3 4 4 3 3 5 4 3 5 3 3 2 3 3 5 3 4 1 5 1 2 1 1 3 1 3 5 6 3 4 6 6 4 2 2 5 1 2 5 4 4 4.0 3 4 2
487 488 22 1 1 2 1 1 2 1 2 2 1 1 1 1 1 2 1 1 1 5 4 3 3 3 5 3 4 4 5 4 4 4 3 3 3 3 4 5 5 4 3 2 4 3 3 2 3 4 5 1 4 3 2 1 1 2 1 1 1 2 1 1 3 3 1 1 3 3 3 5 4 3 5 4 3 5.0 3 4 3
488 489 24 1 3 4 1 1 1 3 4 3 3 1 1 1 1 1 1 4 3 5 5 5 5 5 5 3 2 5 3 4 5 4 5 3 4 4 5 5 5 1 1 5 5 1 1 3 4 5 3 3 5 3 1 3 3 1 3 1 1 2 3 3 1 3 2 1 5 5 4 3 5 5 5 5 4 5.0 5 4 3
489 490 23 1 3 2 1 1 2 5 3 1 1 1 1 1 1 2 1 4 1 5 3 2 2 2 2 2 4 5 4 4 4 4 3 3 3 4 4 4 4 4 3 4 4 4 4 4 4 4 2 3 2 3 2 4 2 3 2 2 2 5 3 5 5 5 5 5 3 3 4 5 4 3 5 3 4 5.0 4 3 3
490 491 22 1 3 1 1 2 1 3 4 1 1 1 1 1 1 2 1 6 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 1 1 1 1 1 1 1 1 1 1 7 7 7 7 7 7 7 4 2 3 5 3 4 4 4 4 4.0 3 4 4
491 492 22 1 3 2 1 1 1 1 4 1 4 1 1 1 1 2 1 1 3 4 4 3 3 3 4 2 4 5 4 3 3 5 3 2 2 3 4 5 3 3 3 4 3 2 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 5 2 2 5 4 3 4.0 4 2 2
492 493 23 1 3 2 1 1 1 1 2 4 3 1 1 1 1 1 1 1 3 4 4 3 3 3 3 3 4 4 4 4 5 5 5 4 4 4 4 4 4 5 5 4 4 4 4 5 4 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 4 3 4 3 3 4 4 3 4.0 3 3 3
493 494 22 1 3 2 2 1 1 1 3 3 2 3 2 2 1 1 2 1 2 5 5 5 4 5 5 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 4 5 5 5 5 5 5 5 5.0 4 5 4
494 495 21 1 3 2 1 1 1 3 4 1 1 1 1 1 1 1 1 5 6 5 5 4 4 5 5 4 5 5 5 5 5 5 5 4 4 4 5 5 5 4 4 4 5 5 5 4 4 5 4 3 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 4 4 2 5 5 5 5 4 2 5.0 2 2 2
495 496 23 1 3 2 1 1 1 1 1 2 3 1 1 1 1 1 1 1 1 5 4 3 3 4 5 3 1 5 5 5 5 5 5 5 5 4 4 5 5 2 1 1 5 1 1 1 4 3 3 3 1 2 1 3 5 4 1 1 5 1 1 1 1 1 1 1 3 2 3 4 4 2 4 3 3 5.0 5 3 1
496 497 24 1 3 6 2 2 1 3 1 2 2 2 1 2 1 1 1 2 2 5 4 3 3 3 5 3 4 4 4 4 4 4 3 3 3 3 3 3 3 3 4 2 4 3 4 3 4 3 1 2 3 2 3 2 3 2 3 2 1 3 3 3 3 3 3 3 5 4 4 4 3 4 3 4 3 4.0 3 3 4
497 498 25 1 3 3 1 1 1 3 1 2 2 2 1 2 1 2 1 4 2 5 4 3 3 3 4 3 3 5 4 4 4 4 3 4 3 4 3 4 3 4 3 2 4 3 3 4 3 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3 4 4 3 3 3 4 3.0 3 3 3
498 499 24 1 3 3 2 1 2 2 2 2 3 2 2 2 1 2 2 2 3 5 4 3 3 3 5 3 4 4 4 4 3 3 3 3 3 3 4 5 4 4 3 3 4 3 3 3 2 4 5 4 5 5 3 5 3 5 5 5 5 3 3 3 3 3 3 3 4 3 3 4 3 3 5 4 3 3.0 4 3 3
499 500 25 1 3 1 1 1 2 1 3 1 1 2 2 2 2 2 1 1 1 4 4 2 3 4 5 2 3 4 5 3 5 4 2 1 2 2 3 3 4 2 2 2 2 4 1 3 5 3 5 3 1 1 5 1 3 1 1 2 1 5 5 5 5 2 1 5 3 4 2 5 1 2 5 3 2 4.0 5 1 3
500 501 21 1 3 2 2 2 1 1 2 3 4 2 3 4 2 2 1 1 4 5 5 5 5 5 5 5 3 5 5 4 4 4 3 3 3 3 3 3 5 2 1 5 4 4 1 1 4 3 5 5 5 5 5 5 5 5 5 5 1 1 1 1 1 1 1 1 5 5 3 5 3 3 5 5 3 5.0 5 5 3
501 502 23 1 3 2 2 1 1 1 2 2 4 1 1 1 1 1 1 1 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 4 3 4 4 4 2 1 1 1 1 1 1 1 1 1 1 6 3 3 4 6 3 3 1 4 1 4 1 1 4 4 4 4.0 4 1 1
502 503 22 1 3 2 1 1 1 5 2 3 3 1 2 1 2 1 1 4 3 4 4 4 4 5 5 4 4 4 3 4 5 4 5 4 4 4 4 3 4 2 2 4 4 3 3 3 3 4 5 1 1 1 5 1 1 1 1 1 1 2 3 1 1 3 1 1 4 3 3 5 3 3 5 4 4 5.0 5 2 1
503 504 26 1 3 2 1 1 2 1 4 1 2 1 1 1 2 1 1 2 2 3 5 4 2 1 4 5 3 5 4 4 5 2 4 3 5 2 1 4 5 4 2 4 1 5 5 5 4 3 3 1 2 2 1 2 5 2 1 2 3 2 3 3 1 4 2 6 3 5 3 4 3 3 5 2 4 1.0 5 4 2
504 505 21 1 3 6 4 1 1 1 2 2 4 2 3 4 2 1 1 2 4 5 5 5 3 5 4 5 1 5 5 5 5 5 5 5 5 4 5 5 5 5 4 5 5 5 5 5 3 5 5 5 1 1 1 1 1 1 1 1 5 3 3 3 3 3 3 3 5 5 3 5 3 4 5 5 4 5.0 5 4 3
505 506 23 1 3 2 2 1 1 4 2 2 2 2 3 3 1 1 2 6 3 4 5 4 1 3 4 5 3 5 4 3 3 3 4 3 4 1 3 4 3 5 3 4 5 4 5 5 5 4 2 3 5 3 2 3 2 4 2 3 2 2 3 2 3 4 5 4 4 5 3 1 3 4 2 4 3 5.0 3 4 2
506 507 23 1 3 2 2 1 1 1 1 2 1 1 1 1 1 1 1 4 3 5 5 5 4 5 5 5 3 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 5 5 4 3 3 1 1 1 5 4 1 1 1 1 5 1 5 5 5 5 5 5 5 4 3 4 2 3 3 2 3 3.0 2 1 2
507 508 21 0 3 4 4 2 1 1 3 3 3 2 2 3 2 1 1 4 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 4 5 5 3 5 5 5 5 5 5 5 1 5 1 1 5 1 1 1 1 1 5 5 5 5 5 5 5 5 5 5 3 5 3 4 5 5 4 5.0 5 4 3
508 509 27 1 3 2 2 1 1 1 4 2 3 1 1 1 1 1 1 2 1 5 4 3 3 3 4 3 5 3 5 4 4 4 3 3 3 3 4 4 4 4 3 3 3 3 3 4 4 3 3 1 1 1 1 1 1 1 1 1 1 3 4 3 2 3 3 3 3 3 4 5 3 3 5 4 4 5.0 3 4 3
509 510 22 1 3 2 4 1 1 4 2 4 4 1 4 3 2 2 1 3 4 4 4 3 2 3 3 1 3 4 4 4 4 4 2 1 1 1 3 3 4 4 4 4 4 4 3 4 4 4 5 5 5 5 5 5 5 5 5 5 5 4 1 4 5 5 1 3 2 2 2 4 1 3 4 5 3 4.0 4 4 2
510 511 22 1 3 3 2 1 2 1 2 2 3 2 2 4 1 1 1 1 4 4 4 4 5 2 1 1 1 5 5 5 5 5 1 4 4 4 4 4 4 4 4 4 4 4 4 4 1 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 1 1 5 4 5 5.0 1 1 1
511 512 21 1 3 4 1 1 1 1 3 1 3 1 2 3 2 1 1 1 3 5 4 5 3 4 3 3 3 5 5 3 4 3 3 3 3 5 3 3 2 2 2 2 3 3 2 2 4 3 5 1 1 1 1 5 5 1 1 1 1 4 1 4 5 1 1 4 4 2 2 4 2 1 4 4 3 5.0 5 2 3
512 513 23 1 3 1 1 1 1 1 3 2 3 1 1 1 1 1 1 1 2 4 4 4 4 4 4 4 2 4 4 4 4 4 4 4 4 4 4 4 3 3 4 3 4 4 3 3 3 3 5 1 1 1 5 5 2 2 1 1 1 1 3 1 3 1 3 1 3 2 2 5 2 2 5 5 5 5.0 4 4 3
513 514 21 0 3 4 2 2 1 1 3 3 4 2 2 2 1 1 2 1 3 5 4 3 3 4 4 3 5 4 5 4 5 5 3 3 2 2 3 3 5 4 4 4 3 3 3 3 4 3 2 1 1 4 1 2 2 3 1 1 1 5 4 1 1 3 1 1 3 2 2 4 3 3 5 5 3 4.0 4 5 3
514 515 22 1 3 6 1 1 1 1 2 4 3 1 1 1 1 2 1 3 3 5 5 2 2 5 3 1 5 5 5 5 5 5 5 1 1 1 1 1 5 5 5 5 1 1 5 5 3 4 5 2 1 2 3 2 5 1 1 1 5 6 5 5 4 6 5 5 4 1 1 4 4 3 5 5 3 5.0 2 4 1
515 516 22 1 4 6 1 1 2 4 4 1 3 1 1 1 1 1 1 1 3 5 5 4 3 4 5 3 4 4 3 3 3 3 2 3 3 3 3 4 4 3 3 3 3 3 3 5 4 4 5 3 1 2 5 1 1 1 1 4 1 1 1 1 5 5 1 1 2 2 1 3 1 1 4 3 3 4.0 2 1 1
516 517 25 0 4 4 4 2 1 1 2 4 6 1 1 1 1 1 4 3 6 5 4 4 3 5 5 1 1 5 5 5 5 5 5 5 3 4 3 3 5 5 5 5 3 5 5 5 5 5 1 1 1 1 1 1 1 1 1 1 1 4 4 4 4 4 4 4 2 2 2 5 2 2 5 5 5 5.0 3 3 3
517 518 18 0 2 4 4 1 1 4 1 3 3 1 1 1 1 2 1 3 3 4 4 4 4 4 4 4 4 5 5 5 4 5 5 4 4 4 4 4 5 5 5 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 3 3 3 5 4 5 5 5 4 5.0 5 5 5
518 519 21 1 3 4 3 1 1 1 1 2 1 1 1 1 1 2 1 1 1 4 4 4 3 3 4 3 4 4 4 4 4 4 4 3 3 3 3 4 3 2 2 2 5 5 2 3 4 4 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 5 4 5 5 5 3 5.0 5 5 5
519 520 22 1 3 4 2 2 1 1 1 2 2 1 1 2 1 2 1 3 2 3 4 4 4 4 4 3 4 5 4 4 4 4 4 4 4 4 4 4 2 2 2 2 5 5 2 3 4 4 5 5 3 1 3 1 3 1 1 1 1 3 3 3 3 3 3 3 4 3 3 5 4 4 5 5 3 5.0 5 5 5
520 521 18 0 2 4 2 2 1 2 2 1 3 1 1 1 1 2 1 5 3 4 3 3 3 4 4 3 4 4 3 4 4 4 3 3 3 3 4 4 4 2 2 2 2 4 2 3 3 4 5 5 1 4 4 4 4 4 1 4 4 6 6 6 6 6 6 6 4 3 3 5 2 5 5 3 3 5.0 5 5 5
521 522 21 1 3 2 1 2 1 1 1 2 3 1 1 1 1 2 1 4 3 4 4 3 3 4 4 3 4 4 4 4 4 4 3 3 3 3 4 4 3 2 2 2 4 4 2 3 4 4 5 5 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 3 3 3 5 4 5 5 3 4 5.0 5 5 5
522 523 20 1 2 4 2 1 1 3 2 2 3 1 1 2 1 2 1 1 3 4 4 3 3 4 4 3 4 4 4 4 4 4 4 3 3 3 4 4 3 2 2 2 5 5 2 3 4 5 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 3 3 3 5 4 4 5 4 4 5.0 5 5 5
523 524 23 1 3 4 3 2 1 3 1 2 3 1 1 1 1 2 1 1 3 5 5 5 4 4 5 3 4 5 4 4 4 4 3 5 3 3 3 5 4 2 2 2 5 5 2 3 4 4 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 3 3 2 5 4 4 5 3 3 5.0 5 5 5
524 525 23 0 3 4 2 1 1 1 1 2 2 1 1 1 1 2 1 2 2 4 4 3 3 3 3 3 4 4 4 4 4 4 4 3 3 3 3 3 4 2 2 2 2 4 1 3 4 4 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 3 2 2 1 5 3 3 5 3 3 5.0 5 5 5
525 526 22 0 3 4 2 1 2 5 4 2 1 1 1 2 1 1 1 2 1 3 4 3 3 4 4 3 3 4 4 4 4 4 4 4 4 3 4 4 3 2 2 2 3 3 3 3 3 3 5 1 1 1 1 1 1 2 2 2 2 3 3 3 1 3 3 4 3 3 3 5 3 3 5 3 3 5.0 5 5 5
526 527 22 0 3 4 4 1 1 5 1 4 3 1 1 2 1 2 1 1 3 5 5 5 4 5 4 4 5 5 5 5 5 5 4 4 4 4 5 5 4 4 4 4 5 5 4 4 4 5 5 5 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 3 3 3 5 3 4 5 4 5 5.0 5 5 5
In [15]:
type(data1)
Out[15]:
pandas.core.frame.DataFrame
In [16]:
data1.columns
Out[16]:
Index(['Record_ID', 'Age', 'Gender', 'Education', 'Father Occupation',
       'Household Income', 'Native (Rural/Urban)',
       'Handset Type(Color/ Black & white)', 'Handset Brand',
       'Handset Selection Reason', 'Handset Cost', 'Handset Age',
       'Calls Receiving per day', 'SMS Receiving per day',
       'SMS Sending per day', 'Frequent Communication Type',
       'Feel safe and secure with mobile', 'Monthly Mobile Expenditure',
       'Current Service Provider', 'Current Service Provider Age',
       'SP - Influence Rating for Signal',
       'SP - Influence Rating for Customer care',
       'SP - Influence Rating for Electronic media',
       'SP - Influence Rating for Hoardings',
       'SP - Influence Rating for Friends',
       'SP - Influence Rating for Family members',
       'SP - Influence Rating for Print media',
       'SP - Influence Rating for Call costs',
       'HS - Influence Rating for Brand',
       'HS - Influence Rating for Battery back up',
       'HS - Influence Rating for Keypad', 'HS - Influence Rating for Display',
       'HS - Influence Rating for Sound',
       'HS - Influence Rating for Size of the screen',
       'HS - Influence Rating for Electronic media',
       'HS - Influence Rating for Print media',
       'HS - Influence Rating for Hoardings',
       'HS - Influence Rating for Friends',
       'HS - Influence Rating for Family members',
       'HS - Influence Rating for Voice clarity',
       'HS - Influence Rating for MP3 facility',
       'HS - Influence Rating for Bluetooth',
       'HS - Influence Rating for Camera pixels',
       'HS - Influence Rating for FM radio', 'HS - Influence Rating for Games',
       'HS - Influence Rating for Video',
       'HS - Influence Rating for Memory size',
       'HS - Influence Rating for Price', 'HS - Influence Rating for Stylish',
       'Stylish Handset Brand', 'More Models Handset Brand',
       'User friendly Handset Brand', 'Reasonable prices Handset Brand',
       'Added features Handset Brand', 'Customer service Handset Brand',
       'Advertisements Handset Brand', 'Sales promotions Handset Brand',
       'Available service centers Handset Brand', 'Durability Handset Brand',
       'Overall, I like the Handset Brand', 'User friendly Service Provider',
       'Added features Service Provider', 'Customer service Service Provider',
       'Advertisements Service Provider', 'Sales promotions Service Provider',
       'Available service centers Service Provider',
       'Overall, I like Service Provider', 'All my friends use',
       'Old fashioned', 'Prestige', 'Secure', 'Show-off', 'Life style',
       'Communication', 'Facilities', 'Life easier', 'Call Friends and Family',
       'Mostly use SMS', 'Music or Videos', 'Games'],
      dtype='object', name='Feature')
In [17]:
data1.describe()
Out[17]:
Feature Record_ID Age Gender Education Father Occupation Household Income Native (Rural/Urban) Handset Type(Color/ Black & white) Handset Brand Handset Selection Reason Handset Cost Handset Age Calls Receiving per day SMS Receiving per day SMS Sending per day Frequent Communication Type Feel safe and secure with mobile Monthly Mobile Expenditure Current Service Provider Current Service Provider Age SP - Influence Rating for Signal SP - Influence Rating for Customer care SP - Influence Rating for Electronic media SP - Influence Rating for Hoardings SP - Influence Rating for Friends SP - Influence Rating for Family members SP - Influence Rating for Print media SP - Influence Rating for Call costs HS - Influence Rating for Brand HS - Influence Rating for Battery back up HS - Influence Rating for Keypad HS - Influence Rating for Display HS - Influence Rating for Sound HS - Influence Rating for Size of the screen HS - Influence Rating for Electronic media HS - Influence Rating for Print media HS - Influence Rating for Hoardings HS - Influence Rating for Friends HS - Influence Rating for Family members HS - Influence Rating for Voice clarity HS - Influence Rating for MP3 facility HS - Influence Rating for Bluetooth HS - Influence Rating for Camera pixels HS - Influence Rating for FM radio HS - Influence Rating for Games HS - Influence Rating for Video HS - Influence Rating for Memory size HS - Influence Rating for Price HS - Influence Rating for Stylish Stylish Handset Brand More Models Handset Brand User friendly Handset Brand Reasonable prices Handset Brand Added features Handset Brand Customer service Handset Brand Advertisements Handset Brand Sales promotions Handset Brand Available service centers Handset Brand Durability Handset Brand Overall, I like the Handset Brand User friendly Service Provider Added features Service Provider Customer service Service Provider Advertisements Service Provider Sales promotions Service Provider Available service centers Service Provider Overall, I like Service Provider All my friends use Old fashioned Prestige Secure Show-off Life style Communication Facilities Life easier Call Friends and Family Mostly use SMS Music or Videos Games
count 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 527.000000 526.000000 527.000000 527.000000 527.000000
mean 264.000000 22.013283 0.719165 2.715370 3.149905 2.146110 1.349146 1.259962 1.963947 2.318786 2.379507 2.891841 1.544592 1.755218 1.796964 1.326376 1.417457 1.411765 2.711575 3.017078 4.318786 3.963947 3.368121 3.218216 3.954459 3.973435 3.146110 3.745731 4.390892 4.233397 4.070209 4.165085 4.123340 3.846300 3.333966 3.233397 3.201139 3.884250 3.948767 3.990512 3.203036 3.111954 3.148008 3.652751 3.612903 3.195446 3.459203 3.867173 3.842505 2.703985 2.316888 1.789374 1.927894 2.278937 1.673624 1.973435 1.770398 1.590133 1.726755 2.075901 2.802657 2.922201 2.777989 2.874763 2.855787 2.759013 2.924099 3.258065 2.821632 2.569260 4.379507 2.842505 3.140417 4.432638 3.975332 3.618596 4.287072 3.711575 3.383302 3.157495
std 152.276065 2.931054 0.449834 0.741627 1.351775 1.020465 0.500489 0.540006 1.316981 0.953724 0.929142 1.389008 0.714731 0.899327 1.006892 0.469332 0.505031 0.736923 1.617490 1.461057 0.787792 0.910767 1.115552 1.138595 0.940135 0.952910 1.130084 1.040919 0.741846 0.829921 0.832334 0.850674 0.859948 0.939774 1.107546 1.117007 1.110509 0.951224 0.928649 0.944216 1.327735 1.335672 1.330010 1.267804 1.098766 1.314492 1.221537 0.951986 1.053627 1.739620 1.647720 1.381312 1.358860 1.647672 1.271106 1.412618 1.308947 1.201606 1.312888 1.601428 1.795366 1.819732 1.794067 1.794443 1.796194 1.885705 1.867759 1.212187 1.102394 1.126269 0.811161 1.251560 1.225218 0.803224 1.019468 1.101621 0.872492 1.147124 1.252050 1.219244
min 1.000000 16.000000 0.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
25% 132.500000 20.000000 0.000000 2.000000 2.000000 1.000000 1.000000 1.000000 1.000000 2.000000 2.000000 2.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 2.000000 4.000000 4.000000 3.000000 2.000000 3.000000 4.000000 2.000000 3.000000 4.000000 4.000000 4.000000 4.000000 4.000000 3.000000 3.000000 3.000000 2.000000 3.000000 4.000000 4.000000 2.000000 2.000000 2.000000 3.000000 3.000000 2.000000 3.000000 3.000000 3.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 2.000000 2.000000 2.000000 4.000000 2.000000 2.000000 4.000000 3.000000 3.000000 4.000000 3.000000 2.000000 2.000000
50% 264.000000 22.000000 1.000000 3.000000 3.000000 2.000000 1.000000 1.000000 1.000000 2.000000 2.000000 3.000000 1.000000 2.000000 1.000000 1.000000 1.000000 1.000000 2.000000 3.000000 4.000000 4.000000 3.000000 3.000000 4.000000 4.000000 3.000000 4.000000 5.000000 4.000000 4.000000 4.000000 4.000000 4.000000 3.000000 3.000000 3.000000 4.000000 4.000000 4.000000 3.000000 3.000000 3.000000 4.000000 4.000000 3.000000 4.000000 4.000000 4.000000 2.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 3.000000 3.000000 3.000000 3.000000 3.000000 2.000000 3.000000 3.000000 3.000000 2.000000 5.000000 3.000000 3.000000 5.000000 4.000000 4.000000 4.000000 4.000000 4.000000 3.000000
75% 395.500000 23.000000 1.000000 3.000000 4.000000 3.000000 2.000000 1.000000 3.000000 3.000000 3.000000 4.000000 2.000000 2.000000 2.000000 2.000000 2.000000 2.000000 4.000000 4.000000 5.000000 5.000000 4.000000 4.000000 5.000000 5.000000 4.000000 4.000000 5.000000 5.000000 5.000000 5.000000 5.000000 4.000000 4.000000 4.000000 4.000000 5.000000 5.000000 5.000000 4.000000 4.000000 4.000000 5.000000 4.000000 4.000000 4.000000 5.000000 5.000000 5.000000 4.000000 2.000000 3.000000 4.000000 2.000000 3.000000 2.000000 1.000000 2.000000 3.000000 4.000000 5.000000 4.000000 4.000000 4.000000 4.000000 4.000000 4.000000 4.000000 3.000000 5.000000 4.000000 4.000000 5.000000 5.000000 4.000000 5.000000 5.000000 4.000000 4.000000
max 527.000000 33.000000 1.000000 4.000000 6.000000 5.000000 5.000000 5.000000 5.000000 4.000000 5.000000 6.000000 4.000000 4.000000 4.000000 2.000000 3.000000 4.000000 7.000000 6.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 7.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000 5.000000
In [18]:
for column in data1.columns:
    print(f"Value counts for {column}:\n{data1[column].value_counts()}\n")
Value counts for Record_ID:
1      1
363    1
361    1
360    1
359    1
358    1
357    1
356    1
355    1
354    1
353    1
352    1
351    1
350    1
349    1
348    1
347    1
346    1
345    1
344    1
343    1
342    1
341    1
340    1
339    1
338    1
337    1
336    1
335    1
334    1
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526    1
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464    1
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398    1
429    1
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461    1
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436    1
435    1
434    1
433    1
432    1
266    1
264    1
2      1
99     1
97     1
96     1
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94     1
93     1
92     1
91     1
90     1
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88     1
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100    1
67     1
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130    1
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102    1
68     1
66     1
132    1
33     1
31     1
30     1
29     1
28     1
27     1
26     1
25     1
24     1
23     1
22     1
21     1
20     1
19     1
18     1
17     1
16     1
15     1
14     1
13     1
12     1
11     1
10     1
9      1
8      1
7      1
6      1
5      1
4      1
3      1
32     1
34     1
65     1
35     1
64     1
63     1
62     1
61     1
60     1
59     1
58     1
57     1
56     1
55     1
54     1
53     1
52     1
51     1
50     1
49     1
48     1
47     1
46     1
45     1
44     1
43     1
42     1
41     1
40     1
39     1
38     1
37     1
36     1
131    1
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263    1
231    1
229    1
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134    1
165    1
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135    1
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176    1
175    1
174    1
173    1
172    1
171    1
170    1
169    1
168    1
527    1
Name: Record_ID, dtype: int64

Value counts for Age:
22    100
21     99
23     51
20     49
19     44
24     34
18     33
26     28
25     24
17     19
29     13
27     12
28     10
30      7
16      3
33      1
Name: Age, dtype: int64

Value counts for Gender:
1    379
0    148
Name: Gender, dtype: int64

Value counts for Education:
3    297
2    139
4     57
1     34
Name: Education, dtype: int64

Value counts for Father Occupation:
2    204
4    162
5     63
3     38
1     37
6     23
Name: Father Occupation, dtype: int64

Value counts for Household Income:
2    177
1    171
3    111
4     67
5      1
Name: Household Income, dtype: int64

Value counts for Native (Rural/Urban):
1    346
2    180
5      1
Name: Native (Rural/Urban), dtype: int64

Value counts for Handset Type(Color/ Black & white):
1    405
2    115
5      3
4      2
3      2
Name: Handset Type(Color/ Black & white), dtype: int64

Value counts for Handset Brand:
1    303
2     67
3     66
4     55
5     36
Name: Handset Brand, dtype: int64

Value counts for Handset Selection Reason:
2    214
3    131
1    109
4     73
Name: Handset Selection Reason, dtype: int64

Value counts for Handset Cost:
2    223
3    143
1     89
4     70
5      2
Name: Handset Cost, dtype: int64

Value counts for Handset Age:
3    172
2    104
1    101
4     84
6     33
5     33
Name: Handset Age, dtype: int64

Value counts for Calls Receiving per day:
1    298
2    182
3     36
4     11
Name: Calls Receiving per day, dtype: int64

Value counts for SMS Receiving per day:
1    257
2    178
3     56
4     36
Name: SMS Receiving per day, dtype: int64

Value counts for SMS Sending per day:
1    278
2    131
3     65
4     53
Name: SMS Sending per day, dtype: int64

Value counts for Frequent Communication Type:
1    355
2    172
Name: Frequent Communication Type, dtype: int64

Value counts for Feel safe and secure with mobile:
1    310
2    214
3      3
Name: Feel safe and secure with mobile, dtype: int64

Value counts for Monthly Mobile Expenditure:
1    371
2    113
3     25
4     18
Name: Monthly Mobile Expenditure, dtype: int64

Value counts for Current Service Provider:
1    176
2     95
4     90
3     83
5     46
6     35
7      2
Name: Current Service Provider, dtype: int64

Value counts for Current Service Provider Age:
3    175
2    105
1     89
4     75
6     51
5     32
Name: Current Service Provider Age, dtype: int64

Value counts for SP - Influence Rating for Signal:
5    250
4    215
3     46
2     12
1      4
Name: SP - Influence Rating for Signal, dtype: int64

Value counts for SP - Influence Rating for Customer care:
4    257
5    151
3     75
2     37
1      7
Name: SP - Influence Rating for Customer care, dtype: int64

Value counts for SP - Influence Rating for Electronic media:
4    179
3    152
5     82
2     79
1     35
Name: SP - Influence Rating for Electronic media, dtype: int64

Value counts for SP - Influence Rating for Hoardings:
3    159
4    158
2     97
5     70
1     43
Name: SP - Influence Rating for Hoardings, dtype: int64

Value counts for SP - Influence Rating for Friends:
4    230
5    162
3     92
2     35
1      8
Name: SP - Influence Rating for Friends, dtype: int64

Value counts for SP - Influence Rating for Family members:
4    232
5    168
3     81
2     37
1      9
Name: SP - Influence Rating for Family members, dtype: int64

Value counts for SP - Influence Rating for Print media:
3    168
4    142
2    109
5     65
1     43
Name: SP - Influence Rating for Print media, dtype: int64

Value counts for SP - Influence Rating for Call costs:
4    217
5    131
3    111
2     50
1     18
Name: SP - Influence Rating for Call costs, dtype: int64

Value counts for HS - Influence Rating for Brand:
5    268
4    211
3     40
1      6
2      2
Name: HS - Influence Rating for Brand, dtype: int64

Value counts for HS - Influence Rating for Battery back up:
5    228
4    220
3     56
2     20
1      3
Name: HS - Influence Rating for Battery back up, dtype: int64

Value counts for HS - Influence Rating for Keypad:
4    242
5    173
3     93
2     14
1      5
Name: HS - Influence Rating for Keypad, dtype: int64

Value counts for HS - Influence Rating for Display:
4    229
5    207
3     67
2     19
1      5
Name: HS - Influence Rating for Display, dtype: int64

Value counts for HS - Influence Rating for Sound:
4    227
5    197
3     79
2     19
1      5
Name: HS - Influence Rating for Sound, dtype: int64

Value counts for HS - Influence Rating for Size of the screen:
4    237
5    131
3    120
2     25
1     14
Name: HS - Influence Rating for Size of the screen, dtype: int64

Value counts for HS - Influence Rating for Electronic media:
4    166
3    165
2     82
5     80
1     34
Name: HS - Influence Rating for Electronic media, dtype: int64

Value counts for HS - Influence Rating for Print media:
3    175
4    153
2     88
5     70
1     41
Name: HS - Influence Rating for Print media, dtype: int64

Value counts for HS - Influence Rating for Hoardings:
3    172
4    159
2     91
5     62
1     43
Name: HS - Influence Rating for Hoardings, dtype: int64

Value counts for HS - Influence Rating for Friends:
4    241
5    141
3    101
2     31
1     13
Name: HS - Influence Rating for Friends, dtype: int64

Value counts for HS - Influence Rating for Family members:
4    243
5    154
3     88
2     33
1      9
Name: HS - Influence Rating for Family members, dtype: int64

Value counts for HS - Influence Rating for Voice clarity:
4    236
5    169
3     81
2     30
1     11
Name: HS - Influence Rating for Voice clarity, dtype: int64

Value counts for HS - Influence Rating for MP3 facility:
4    139
2    118
5    108
3     97
1     65
Name: HS - Influence Rating for MP3 facility, dtype: int64

Value counts for HS - Influence Rating for Bluetooth:
2    123
4    118
3    110
5    104
1     72
Name: HS - Influence Rating for Bluetooth, dtype: int64

Value counts for HS - Influence Rating for Camera pixels:
4    132
2    122
5    103
3    101
1     69
Name: HS - Influence Rating for Camera pixels, dtype: int64

Value counts for HS - Influence Rating for FM radio:
4    179
5    162
2     75
3     69
1     42
Name: HS - Influence Rating for FM radio, dtype: int64

Value counts for HS - Influence Rating for Games:
4    196
3    126
5    119
2     61
1     25
Name: HS - Influence Rating for Games, dtype: int64

Value counts for HS - Influence Rating for Video:
4    146
2    111
3    102
5    101
1     67
Name: HS - Influence Rating for Video, dtype: int64

Value counts for HS - Influence Rating for Memory size:
4    182
3    114
5    113
2     70
1     48
Name: HS - Influence Rating for Memory size, dtype: int64

Value counts for HS - Influence Rating for Price:
4    241
5    137
3    104
2     32
1     13
Name: HS - Influence Rating for Price, dtype: int64

Value counts for HS - Influence Rating for Stylish:
4    205
5    159
3    103
2     41
1     19
Name: HS - Influence Rating for Stylish, dtype: int64

Value counts for Stylish Handset Brand:
1    231
5    162
3     68
2     42
4     24
Name: Stylish Handset Brand, dtype: int64

Value counts for More Models Handset Brand:
1    288
5    115
3     62
2     38
4     24
Name: More Models Handset Brand, dtype: int64

Value counts for User friendly Handset Brand:
1    372
5     56
3     35
2     35
4     29
Name: User friendly Handset Brand, dtype: int64

Value counts for Reasonable prices Handset Brand:
1    321
2     65
4     56
5     43
3     42
Name: Reasonable prices Handset Brand, dtype: int64

Value counts for Added features Handset Brand:
1    295
5    112
3     46
2     44
4     30
Name: Added features Handset Brand, dtype: int64

Value counts for Customer service Handset Brand:
1    390
4     38
5     37
2     31
3     31
Name: Customer service Handset Brand, dtype: int64

Value counts for Advertisements Handset Brand:
1    321
3     65
5     60
2     50
4     31
Name: Advertisements Handset Brand, dtype: int64

Value counts for Sales promotions Handset Brand:
1    363
2     43
3     41
5     41
4     39
Name: Sales promotions Handset Brand, dtype: int64

Value counts for Available service centers Handset Brand:
1    404
5     34
3     34
2     29
4     26
Name: Available service centers Handset Brand, dtype: int64

Value counts for Durability Handset Brand:
1    376
5     49
3     41
2     39
4     22
Name: Durability Handset Brand, dtype: int64

Value counts for Overall, I like the Handset Brand:
1    341
5     97
3     42
4     24
2     23
Name: Overall, I like the Handset Brand, dtype: int64

Value counts for User friendly Service Provider:
1    207
5     79
3     76
4     68
2     54
6     34
7      9
Name: User friendly Service Provider, dtype: int64

Value counts for Added features Service Provider:
1    194
5     94
3     88
4     62
2     46
6     29
7     14
Name: Added features Service Provider, dtype: int64

Value counts for Customer service Service Provider:
1    218
4     78
3     76
5     71
2     40
6     37
7      7
Name: Customer service Service Provider, dtype: int64

Value counts for Advertisements Service Provider:
1    210
4     91
5     85
3     71
6     32
2     31
7      7
Name: Advertisements Service Provider, dtype: int64

Value counts for Sales promotions Service Provider:
1    202
3     94
5     76
4     64
6     42
2     42
7      7
Name: Sales promotions Service Provider, dtype: int64

Value counts for Available service centers Service Provider:
1    236
3     78
5     72
6     51
4     49
2     33
7      8
Name: Available service centers Service Provider, dtype: int64

Value counts for Overall, I like Service Provider:
1    205
3     84
4     74
5     70
6     45
2     35
7     14
Name: Overall, I like Service Provider, dtype: int64

Value counts for All my friends use:
4    148
2    124
3    121
5     95
1     39
Name: All my friends use, dtype: int64

Value counts for Old fashioned:
2    178
3    138
4    122
1     54
5     35
Name: Old fashioned, dtype: int64

Value counts for Prestige:
2    214
3    122
1     81
4     71
5     39
Name: Prestige, dtype: int64

Value counts for Secure:
5    283
4    182
3     48
1      7
2      7
Name: Secure, dtype: int64

Value counts for Show-off:
2    171
3    125
4     86
5     73
1     72
Name: Show-off, dtype: int64

Value counts for Life style:
4    142
2    134
3    120
5     82
1     49
Name: Life style, dtype: int64

Value counts for Communication:
5    304
4    169
3     39
2      8
1      7
Name: Communication, dtype: int64

Value counts for Facilities:
4    203
5    188
3     85
2     37
1     14
Name: Facilities, dtype: int64

Value counts for Life easier:
4    194
5    122
3    122
2     66
1     23
Name: Life easier, dtype: int64

Value counts for Call Friends and Family:
5.0    260
4.0    187
3.0     56
2.0     16
1.0      7
Name: Call Friends and Family, dtype: int64

Value counts for Mostly use SMS:
4    161
5    161
3    120
2     62
1     23
Name: Mostly use SMS, dtype: int64

Value counts for Music or Videos:
4    154
5    118
3    111
2    100
1     44
Name: Music or Videos, dtype: int64

Value counts for Games:
3    152
4    123
2    112
5     88
1     52
Name: Games, dtype: int64

In [19]:
data1.iloc[:, 1].unique()
Out[19]:
array([21, 24, 22, 26, 28, 23, 25, 17, 29, 20, 30, 27, 33, 16, 18, 19],
      dtype=int64)
In [20]:
grouped_data = data1.groupby('Education')['Education'].count()
In [21]:
grouped_data
Out[21]:
Education
1     34
2    139
3    297
4     57
Name: Education, dtype: int64
In [22]:
data1.iloc[:,5].value_counts()
Out[22]:
2    177
1    171
3    111
4     67
5      1
Name: Household Income, dtype: int64
In [23]:
# Checking condition
condition = data1['Household Income'] >= 5

# Update values in the specified column based on the condition
data1.loc[condition, 'Household Income'] = 4  # Replace 'updated_value' with the value you want to set
In [24]:
data1.iloc[:,10].value_counts()
Out[24]:
2    223
3    143
1     89
4     70
5      2
Name: Handset Cost, dtype: int64
In [25]:
# Checking condition
condition = data1['Household Income'] >= 5

# Update values in the specified column based on the condition
data1.loc[condition, 'Household Income'] = 4  # Replace 'updated_value' with the value you want to set
In [26]:
data1[(data1.iloc[:,10]==5) & (data1.iloc[:,6]==2)]
Out[26]:
Feature Record_ID Age Gender Education Father Occupation Household Income Native (Rural/Urban) Handset Type(Color/ Black & white) Handset Brand Handset Selection Reason Handset Cost Handset Age Calls Receiving per day SMS Receiving per day SMS Sending per day Frequent Communication Type Feel safe and secure with mobile Monthly Mobile Expenditure Current Service Provider Current Service Provider Age SP - Influence Rating for Signal SP - Influence Rating for Customer care SP - Influence Rating for Electronic media SP - Influence Rating for Hoardings SP - Influence Rating for Friends SP - Influence Rating for Family members SP - Influence Rating for Print media SP - Influence Rating for Call costs HS - Influence Rating for Brand HS - Influence Rating for Battery back up HS - Influence Rating for Keypad HS - Influence Rating for Display HS - Influence Rating for Sound HS - Influence Rating for Size of the screen HS - Influence Rating for Electronic media HS - Influence Rating for Print media HS - Influence Rating for Hoardings HS - Influence Rating for Friends HS - Influence Rating for Family members HS - Influence Rating for Voice clarity HS - Influence Rating for MP3 facility HS - Influence Rating for Bluetooth HS - Influence Rating for Camera pixels HS - Influence Rating for FM radio HS - Influence Rating for Games HS - Influence Rating for Video HS - Influence Rating for Memory size HS - Influence Rating for Price HS - Influence Rating for Stylish Stylish Handset Brand More Models Handset Brand User friendly Handset Brand Reasonable prices Handset Brand Added features Handset Brand Customer service Handset Brand Advertisements Handset Brand Sales promotions Handset Brand Available service centers Handset Brand Durability Handset Brand Overall, I like the Handset Brand User friendly Service Provider Added features Service Provider Customer service Service Provider Advertisements Service Provider Sales promotions Service Provider Available service centers Service Provider Overall, I like Service Provider All my friends use Old fashioned Prestige Secure Show-off Life style Communication Facilities Life easier Call Friends and Family Mostly use SMS Music or Videos Games
249 250 24 1 2 3 1 2 1 3 3 5 2 2 2 1 1 3 1 1 6 5 5 5 5 5 5 5 5 4 5 5 5 5 4 4 5 5 5 5 5 5 5 4 4 4 3 4 4 4 3 4 3 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 5 5 5 5 5 5 5.0 4 5 3
In [27]:
# Your conditions
condition1 = (data1['Handset Cost'] == 5)
condition2 = (data1['Native (Rural/Urban)'] == 2)  # Add additional conditions as needed

# Update values in the specified column based on the conditions
data1.loc[condition1 & condition2, 'Handset Cost'] = 2  # Replace 'updated_value' with the value you want to set
In [28]:
# Your conditions
condition1 = (data1['Handset Cost'] == 5)
condition2 = (data1['Native (Rural/Urban)'] == 1)  # Add additional conditions as needed

# Update values in the specified column based on the conditions
data1.loc[condition1 & condition2, 'Handset Cost'] = 3  # Replace 'updated_value' with the value you want to set
In [29]:
data1.iloc[:,10].value_counts()
Out[29]:
2    224
3    144
1     89
4     70
Name: Handset Cost, dtype: int64
In [30]:
data1[(data1['Native (Rural/Urban)']>=3)]
Out[30]:
Feature Record_ID Age Gender Education Father Occupation Household Income Native (Rural/Urban) Handset Type(Color/ Black & white) Handset Brand Handset Selection Reason Handset Cost Handset Age Calls Receiving per day SMS Receiving per day SMS Sending per day Frequent Communication Type Feel safe and secure with mobile Monthly Mobile Expenditure Current Service Provider Current Service Provider Age SP - Influence Rating for Signal SP - Influence Rating for Customer care SP - Influence Rating for Electronic media SP - Influence Rating for Hoardings SP - Influence Rating for Friends SP - Influence Rating for Family members SP - Influence Rating for Print media SP - Influence Rating for Call costs HS - Influence Rating for Brand HS - Influence Rating for Battery back up HS - Influence Rating for Keypad HS - Influence Rating for Display HS - Influence Rating for Sound HS - Influence Rating for Size of the screen HS - Influence Rating for Electronic media HS - Influence Rating for Print media HS - Influence Rating for Hoardings HS - Influence Rating for Friends HS - Influence Rating for Family members HS - Influence Rating for Voice clarity HS - Influence Rating for MP3 facility HS - Influence Rating for Bluetooth HS - Influence Rating for Camera pixels HS - Influence Rating for FM radio HS - Influence Rating for Games HS - Influence Rating for Video HS - Influence Rating for Memory size HS - Influence Rating for Price HS - Influence Rating for Stylish Stylish Handset Brand More Models Handset Brand User friendly Handset Brand Reasonable prices Handset Brand Added features Handset Brand Customer service Handset Brand Advertisements Handset Brand Sales promotions Handset Brand Available service centers Handset Brand Durability Handset Brand Overall, I like the Handset Brand User friendly Service Provider Added features Service Provider Customer service Service Provider Advertisements Service Provider Sales promotions Service Provider Available service centers Service Provider Overall, I like Service Provider All my friends use Old fashioned Prestige Secure Show-off Life style Communication Facilities Life easier Call Friends and Family Mostly use SMS Music or Videos Games
62 63 20 1 2 4 4 5 1 5 2 3 4 2 2 2 1 1 2 4 3 5 5 4 3 3 3 3 3 5 5 5 5 5 4 4 3 3 2 2 1 1 1 1 1 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 5 4 5 3 5 3 5 2 5 1.0 5 5 3
In [31]:
# Checking condition
condition = data1['Native (Rural/Urban)'] >= 3

# Update values in the specified column based on the condition
data1.loc[condition, 'Native (Rural/Urban)'] = 2  # Replace 'updated_value' with the value you want to set
In [32]:
data1['Native (Rural/Urban)'].value_counts()
Out[32]:
1    346
2    181
Name: Native (Rural/Urban), dtype: int64
In [33]:
data1[(data1['Frequent Communication Type']>=2)]
Out[33]:
Feature Record_ID Age Gender Education Father Occupation Household Income Native (Rural/Urban) Handset Type(Color/ Black & white) Handset Brand Handset Selection Reason Handset Cost Handset Age Calls Receiving per day SMS Receiving per day SMS Sending per day Frequent Communication Type Feel safe and secure with mobile Monthly Mobile Expenditure Current Service Provider Current Service Provider Age SP - Influence Rating for Signal SP - Influence Rating for Customer care SP - Influence Rating for Electronic media SP - Influence Rating for Hoardings SP - Influence Rating for Friends SP - Influence Rating for Family members SP - Influence Rating for Print media SP - Influence Rating for Call costs HS - Influence Rating for Brand HS - Influence Rating for Battery back up HS - Influence Rating for Keypad HS - Influence Rating for Display HS - Influence Rating for Sound HS - Influence Rating for Size of the screen HS - Influence Rating for Electronic media HS - Influence Rating for Print media HS - Influence Rating for Hoardings HS - Influence Rating for Friends HS - Influence Rating for Family members HS - Influence Rating for Voice clarity HS - Influence Rating for MP3 facility HS - Influence Rating for Bluetooth HS - Influence Rating for Camera pixels HS - Influence Rating for FM radio HS - Influence Rating for Games HS - Influence Rating for Video HS - Influence Rating for Memory size HS - Influence Rating for Price HS - Influence Rating for Stylish Stylish Handset Brand More Models Handset Brand User friendly Handset Brand Reasonable prices Handset Brand Added features Handset Brand Customer service Handset Brand Advertisements Handset Brand Sales promotions Handset Brand Available service centers Handset Brand Durability Handset Brand Overall, I like the Handset Brand User friendly Service Provider Added features Service Provider Customer service Service Provider Advertisements Service Provider Sales promotions Service Provider Available service centers Service Provider Overall, I like Service Provider All my friends use Old fashioned Prestige Secure Show-off Life style Communication Facilities Life easier Call Friends and Family Mostly use SMS Music or Videos Games
1 2 24 1 3 2 1 1 1 3 2 3 2 1 1 1 2 1 1 3 2 4 5 4 3 4 4 3 3 3 3 5 4 4 5 4 4 4 5 3 3 1 1 1 5 3 1 2 4 5 3 5 1 4 5 1 4 1 1 1 1 3 7 5 1 1 1 3 5 1 3 5 2 5 5 3 4 5.0 3 2 3
3 4 21 1 3 2 1 1 1 1 1 2 3 1 1 1 2 2 1 1 3 5 4 4 4 4 4 4 4 4 4 4 4 3 4 4 3 4 4 4 4 4 4 3 4 4 4 4 4 4 4 4 4 4 4 1 1 1 1 1 1 4 4 4 4 4 4 4 4 4 3 4 4 2 4 4 4 4.0 4 5 4
4 5 21 1 3 2 3 1 1 1 2 2 1 2 3 3 2 2 2 3 2 5 5 5 5 5 5 5 5 5 4 5 5 5 5 5 5 5 4 5 5 5 3 3 5 5 3 3 5 5 1 1 1 1 1 1 1 1 1 1 1 4 4 4 4 3 3 4 4 4 4 4 4 5 5 5 5 5.0 4 5 5
5 6 22 1 3 3 1 1 1 2 2 3 3 1 2 3 2 1 1 4 2 4 5 4 3 5 5 3 4 5 5 4 4 5 4 3 3 3 4 5 4 5 4 4 4 4 4 4 4 4 5 1 1 4 4 4 1 3 4 1 5 5 7 5 1 1 1 1 5 4 3 4 2 2 4 5 4 4.0 5 5 3
9 10 22 1 2 4 3 1 1 4 2 3 3 2 2 2 2 1 1 3 3 5 5 3 3 5 5 3 4 4 5 4 4 3 2 3 3 3 5 5 5 3 1 1 1 2 2 4 4 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 5 2 4 5 4 4 5.0 3 4 2
13 14 22 1 3 4 1 2 1 1 1 2 5 2 2 4 2 1 1 4 3 4 3 4 4 5 5 4 4 5 5 5 5 5 4 4 4 4 5 5 4 4 3 4 4 3 3 4 3 3 1 1 1 1 5 1 1 1 1 1 1 1 3 1 5 6 5 5 4 3 2 5 2 2 5 4 3 3.0 3 2 1
14 15 22 1 3 4 2 2 1 1 2 3 3 3 4 4 2 1 1 2 3 5 5 5 4 3 4 4 5 4 5 5 5 5 4 4 4 3 4 4 5 5 5 5 5 5 5 5 4 4 1 5 1 1 1 1 1 1 1 1 1 3 6 1 1 5 5 6 3 3 3 4 2 2 5 5 3 5.0 4 5 3
15 16 21 1 3 2 2 1 2 1 2 1 3 1 2 2 2 2 1 2 1 5 4 3 2 4 5 3 5 5 4 3 4 4 4 2 2 2 4 5 3 3 3 3 3 3 3 4 5 4 4 1 1 1 1 1 3 1 5 1 1 2 3 1 5 3 1 1 4 4 4 3 2 2 4 2 3 5.0 5 2 3
19 20 26 1 3 4 2 1 2 5 4 2 2 3 4 4 2 1 2 3 2 5 4 3 3 4 4 3 2 3 4 3 3 2 2 4 3 2 2 2 4 1 3 2 3 4 2 1 4 2 3 1 1 3 5 1 1 5 1 5 5 4 4 4 1 1 1 4 2 3 2 5 2 2 5 4 4 3.0 3 2 3
33 34 22 1 3 2 2 1 1 5 4 2 3 1 1 1 2 2 1 5 3 4 4 4 4 4 4 4 4 5 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 4 3 1 1 1 1 1 1 1 2 4 2 2 2 2 2 5 3 4 3 5 5 5 5 5 5.0 5 5 5
34 35 24 1 3 2 1 1 2 1 2 1 3 1 2 2 2 1 1 2 2 5 3 3 3 3 3 3 3 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 2 1 1 1 2 3 2 3 1 3 1 3 3 1 3 5 3 3 3 3 3 3 4 3 4 3 5 3 5.0 4 3 3
35 36 24 1 3 2 1 1 1 3 2 2 5 2 1 1 2 2 2 1 5 5 5 4 3 5 3 4 1 5 4 3 5 4 3 5 4 3 5 5 4 3 5 3 5 3 5 4 3 5 1 4 3 2 1 2 3 4 5 5 5 2 2 2 3 2 2 2 5 4 3 4 5 3 4 5 4 3.0 5 4 3
37 38 21 1 4 2 1 1 1 1 2 2 2 4 4 4 2 2 3 2 2 4 4 4 4 5 5 4 3 5 4 3 3 4 4 4 4 3 4 4 3 3 3 4 4 4 3 4 5 2 5 5 1 1 1 1 1 1 1 5 1 2 7 3 4 6 4 4 4 4 5 5 4 3 5 5 3 4.0 5 4 5
38 39 24 1 3 2 2 2 1 4 4 2 3 3 3 4 2 1 4 4 4 5 5 4 4 3 3 2 2 5 4 3 5 5 5 4 4 3 3 4 4 5 5 4 4 5 4 3 4 5 3 4 5 5 5 5 3 1 2 3 4 1 2 3 4 1 7 7 4 4 4 3 2 1 1 1 1 2.0 3 4 5
41 42 23 1 3 2 1 1 1 3 2 3 2 1 2 2 2 1 2 2 3 4 1 3 3 3 3 1 3 5 3 2 3 2 2 3 3 3 2 4 2 2 3 2 1 4 3 1 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 3 3 3 3 3 3 3 3 3.0 3 3 3
42 43 25 1 3 2 2 1 2 2 3 1 1 1 1 1 2 1 1 3 2 5 5 5 5 5 5 5 5 5 4 5 4 5 4 5 4 5 4 5 4 2 3 4 5 4 3 4 5 4 2 3 2 3 2 1 2 5 5 5 5 1 2 3 4 5 6 7 2 2 1 1 2 3 4 5 3 2.0 3 2 1
45 46 24 1 3 2 3 1 1 2 3 3 4 1 2 3 2 1 1 2 3 3 3 3 3 3 3 3 5 4 4 4 5 3 5 3 3 3 4 2 5 5 3 3 3 3 2 4 4 4 4 5 1 4 4 4 3 1 1 1 1 3 2 2 1 1 1 1 2 2 2 5 2 3 5 4 4 5.0 5 4 4
50 51 21 1 3 2 1 1 2 3 2 1 2 1 1 3 2 2 1 5 2 5 4 4 3 4 3 2 4 4 4 4 5 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 3 3 3 5 3 4 4 3 2 3.0 3 4 4
58 59 22 1 3 2 1 1 4 1 4 4 2 2 2 3 2 1 2 1 4 5 5 3 4 5 4 3 3 5 4 3 5 3 5 2 4 3 5 4 5 3 2 4 5 3 5 4 3 5 5 1 1 5 2 1 5 4 1 2 3 1 4 3 6 7 2 5 3 4 3 5 4 3 5 4 4 4.0 3 4 4
63 64 25 1 3 2 1 1 1 4 3 3 3 1 1 1 2 1 2 3 4 5 5 4 4 4 4 3 3 5 5 4 4 3 3 3 3 4 4 5 5 5 5 5 5 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 1 1 1 2 3 3 3 5 4 5 3 5 3 5 3 5 2.0 5 1 3
66 67 22 1 3 5 3 1 2 4 4 2 1 4 4 4 2 1 1 2 1 3 5 4 3 5 3 2 4 5 4 5 4 3 3 4 2 5 5 5 3 1 1 1 5 5 3 4 3 3 5 5 5 5 5 5 5 5 5 5 1 1 3 3 3 3 3 3 3 4 3 5 3 2 5 3 4 4.0 5 5 3
67 68 22 1 3 2 2 1 1 1 3 1 3 1 2 3 2 1 2 4 3 5 1 4 4 3 2 5 5 5 4 5 4 4 4 5 5 5 5 4 4 4 4 4 4 3 4 3 3 3 1 1 1 1 2 1 1 1 1 1 1 5 1 1 1 1 1 1 5 5 5 4 5 4 5 5 4 5.0 5 3 3
68 69 22 1 3 2 3 2 2 1 4 3 1 1 4 4 2 2 1 2 2 1 4 4 5 4 5 3 3 5 4 5 3 4 3 5 3 5 5 5 3 4 4 4 5 4 5 3 4 5 1 1 1 1 1 1 4 4 1 1 5 3 1 3 1 3 3 3 3 4 3 5 3 2 5 4 5 4.0 5 4 3
69 70 22 1 3 2 3 1 1 1 2 3 4 3 3 4 2 2 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 1 5 5 5 5 1 1 1 1 1 5 5 5 5 5 5 5 4 2 1 4 4 2 4 4 4 4.0 4 4 2
70 71 23 1 3 2 1 1 1 1 2 2 1 1 2 2 2 2 1 2 1 4 4 4 4 4 4 4 4 4 4 4 4 5 4 4 4 4 4 4 5 3 4 4 4 4 4 4 4 4 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 3 4 2 3 4 4 4 5 4 3 4.0 2 1 2
71 72 23 1 2 2 1 1 1 1 2 2 3 1 3 1 2 2 1 1 5 4 5 3 3 4 3 3 2 4 5 4 4 4 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 1 1 1 3 1 1 1 1 1 1 1 1 5 1 4 1 1 1 4 2 2 4 2 3 4 3 4 5.0 4 3 2
73 74 22 1 3 1 1 1 1 1 2 2 5 1 1 1 2 1 2 1 2 5 5 4 5 3 3 3 3 4 5 4 5 4 4 4 4 5 5 5 5 3 3 3 5 5 3 5 4 5 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 3 3 5 5 5 5 5 4 5.0 5 3 5
76 77 22 1 3 4 2 2 1 1 1 2 3 3 3 3 2 2 2 4 2 5 4 5 4 5 5 5 4 5 5 5 5 5 5 5 5 4 5 5 5 4 5 4 5 5 5 3 4 4 1 5 1 5 1 1 1 1 1 1 1 5 1 1 1 1 1 1 4 2 2 5 2 2 4 2 2 5.0 5 2 2
77 78 21 1 3 2 3 1 1 2 4 2 2 1 1 3 2 2 1 1 3 5 4 3 2 2 4 4 3 4 4 3 4 5 4 3 3 2 2 4 4 3 4 3 5 3 3 3 4 5 5 1 4 1 5 1 4 1 5 5 1 1 1 2 2 3 1 1 4 4 1 5 4 4 5 4 4 4.0 4 3 3
81 82 25 1 3 2 3 1 3 1 3 1 3 1 2 3 2 1 1 7 1 3 3 3 4 3 4 3 4 3 4 3 2 5 3 2 3 5 4 3 2 1 2 3 3 3 3 4 4 3 5 2 1 1 5 1 2 1 1 3 1 1 4 1 5 1 1 1 2 2 2 4 2 4 4 4 4 4.0 3 3 2
83 84 22 1 3 2 1 1 1 1 3 3 1 2 2 3 2 2 1 2 1 3 4 4 4 5 5 5 4 5 4 5 4 4 4 4 4 4 5 5 4 3 2 1 3 1 1 2 1 4 1 1 1 2 3 1 1 1 1 1 1 3 3 3 3 3 3 3 3 3 1 5 1 1 5 4 1 5.0 4 1 2
84 85 23 1 3 2 1 1 2 1 3 2 2 1 1 1 2 2 1 2 1 4 4 5 5 5 5 4 4 5 4 3 5 3 4 4 3 5 4 4 2 3 4 3 4 3 5 4 4 3 5 5 1 1 5 1 1 1 1 1 1 5 3 1 3 3 3 2 5 4 3 5 4 2 3 3 2 2.0 5 2 1
85 86 21 1 3 2 1 1 1 1 2 2 5 2 3 3 2 2 1 2 3 5 3 4 4 4 4 3 5 5 5 5 5 5 5 4 4 4 4 4 5 4 5 4 4 4 4 5 4 4 1 1 1 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 4 4 4 4 4 4 4 4 4 4.0 4 4 4
87 88 22 1 3 2 3 1 2 2 2 2 2 1 1 2 2 2 1 5 1 4 4 3 4 4 3 3 4 5 3 3 4 3 2 3 3 3 4 4 2 1 1 1 1 1 1 1 4 4 4 3 2 4 4 4 5 4 3 3 4 2 2 6 6 6 5 6 3 2 2 4 1 2 4 4 3 4.0 4 2 1
92 93 22 1 3 2 2 2 1 1 2 3 3 1 1 1 2 2 2 2 1 2 3 3 2 4 4 4 3 4 4 4 4 3 3 3 2 4 4 3 3 2 2 2 3 4 3 3 3 2 3 4 2 1 4 1 3 2 1 4 4 6 1 1 2 2 6 4 4 3 3 2 3 4 5 4 3 3.0 4 4 4
96 97 30 1 4 2 1 1 1 3 2 3 3 2 2 2 2 2 2 2 1 2 2 4 4 4 4 4 3 4 3 3 4 4 4 4 2 2 4 4 4 2 2 2 4 4 2 5 4 4 1 5 1 1 1 1 1 1 1 1 1 1 1 2 3 4 3 3 4 2 4 4 3 4 4 4 4 5.0 5 2 1
100 101 29 1 4 4 4 1 1 1 2 4 6 3 3 1 2 1 4 2 5 4 3 3 2 5 3 4 4 5 5 4 4 4 4 4 3 3 3 5 4 4 4 4 3 3 4 3 5 4 3 1 2 1 2 2 2 2 2 3 2 3 3 3 3 3 3 3 1 4 1 5 3 3 5 4 4 4.0 4 3 2
101 102 26 1 4 4 3 2 1 4 2 4 2 2 3 2 2 2 2 1 4 4 3 3 4 3 3 3 3 5 3 5 5 5 5 4 4 4 4 4 5 5 5 5 5 5 5 5 3 5 5 5 5 2 5 1 1 5 1 1 5 3 3 3 1 3 1 3 5 3 2 5 3 4 5 5 5 5.0 5 5 4
108 109 26 1 4 4 1 1 1 1 3 2 4 1 1 2 2 2 1 1 4 5 5 3 3 4 3 3 4 5 4 4 5 5 4 4 4 3 4 4 5 4 2 2 3 4 2 2 3 4 3 1 1 1 5 1 1 1 1 3 1 1 6 1 5 3 1 1 4 2 2 3 1 2 5 4 3 4.0 3 3 2
113 114 26 1 4 4 4 2 1 4 2 4 2 1 2 2 2 1 2 1 2 4 4 5 4 4 4 4 3 4 4 3 5 5 4 4 4 4 4 4 4 5 4 5 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 1 1 1 1 1 1 1 3 3 3 5 3 3 5 4 4 4.0 4 5 4
127 128 27 1 4 5 3 2 2 3 4 1 4 2 3 3 2 1 1 6 5 5 5 5 4 4 5 4 5 4 5 3 3 4 5 2 1 1 3 5 5 1 1 2 2 2 2 2 5 2 3 1 1 1 1 1 3 1 1 1 1 1 1 2 1 1 1 1 2 4 1 5 1 1 5 5 5 5.0 4 1 2
128 129 30 1 4 2 1 1 2 1 4 2 4 1 1 1 2 1 1 1 4 5 3 3 4 2 1 3 5 5 4 5 5 5 5 5 5 5 5 4 5 2 2 2 2 4 2 2 4 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 1 1 1 1 1 1 1 1 1 1 1 1.0 1 1 1
130 131 25 1 4 1 1 1 2 1 4 2 4 1 1 1 2 1 1 1 4 5 4 4 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 1 1 1 1 1 1 1 1 1 3 3 3 5 3 3 3 5 5 5 5 5 5 5 5 5 5.0 5 5 5
137 138 26 1 4 2 3 1 1 1 4 3 3 1 1 1 2 1 1 2 3 4 4 5 2 4 4 3 4 4 4 4 4 4 3 3 3 3 4 3 4 3 3 3 4 3 3 5 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 5 3 2 4 2 2 4 3 3 5.0 4 3 2
139 140 26 1 4 2 4 1 1 1 1 4 3 2 4 4 2 1 3 3 3 5 5 4 3 4 3 3 5 5 5 4 4 3 5 4 3 3 4 4 4 4 4 5 4 2 4 5 3 5 1 1 1 1 1 1 1 1 1 1 1 4 4 4 4 4 4 4 3 3 2 4 2 4 5 5 5 5.0 5 3 2
145 146 24 1 3 4 3 2 1 1 1 4 6 3 4 4 2 1 4 4 6 4 4 5 5 4 5 4 3 5 5 5 5 5 5 3 3 3 3 3 5 5 5 5 5 4 5 5 5 5 5 1 1 1 5 1 5 1 1 1 1 1 5 1 5 5 5 5 5 5 4 5 5 5 5 5 5 5.0 5 4 3
151 152 26 1 3 2 1 1 2 3 4 1 4 3 3 1 2 2 1 5 4 4 3 2 2 4 5 2 4 4 4 4 4 4 4 2 2 2 4 4 4 2 2 2 2 2 2 2 5 4 1 1 1 1 1 1 1 1 1 1 1 4 4 4 4 4 4 4 5 5 3 4 4 3 5 5 5 5.0 5 3 3
160 161 23 1 3 2 2 1 1 1 2 3 1 2 3 3 2 2 2 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 3 4 3 4 4 3 4 4 4 4.0 4 4 3
165 166 22 1 3 4 4 2 2 3 4 1 1 1 1 1 2 1 1 6 3 4 4 4 4 4 4 5 5 4 5 5 5 5 5 4 4 4 5 5 4 5 5 5 5 4 5 5 4 5 1 1 5 3 1 1 5 1 1 1 1 2 2 2 2 2 2 2 5 5 5 4 5 4 5 4 5 5.0 5 4 2
166 167 24 1 3 2 3 1 1 1 1 3 4 2 4 4 2 1 1 2 3 2 1 4 2 2 4 3 5 5 5 5 5 4 5 5 5 3 5 4 5 5 5 5 5 5 5 5 5 5 1 1 1 1 5 2 1 3 1 1 5 2 1 4 5 1 1 2 5 1 2 5 4 4 5 5 4 5.0 5 3 3
169 170 24 1 3 4 3 1 1 1 2 4 4 1 1 1 2 1 2 1 5 5 4 4 4 4 3 2 2 5 5 4 5 5 5 4 4 4 4 4 4 5 4 5 4 4 5 4 5 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 5 2 2 5 5 5 5.0 5 5 5
170 171 22 0 3 4 1 1 1 1 2 2 3 1 1 1 2 2 1 1 3 5 5 4 4 4 4 4 4 5 4 4 4 4 4 4 4 4 4 4 4 2 2 2 4 4 2 2 3 3 1 3 5 1 5 1 2 1 1 1 1 1 1 1 1 1 1 1 4 2 2 5 2 3 5 4 4 4.0 2 3 3
171 172 22 0 3 2 1 2 1 1 2 3 3 1 3 3 2 2 1 2 3 4 4 3 3 4 4 3 4 5 4 4 5 3 3 4 3 3 4 4 4 4 4 4 5 3 3 2 4 4 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 3 4 3 3 4 2 2 4 4 4 5.0 5 5 2
173 174 25 1 3 5 2 2 2 1 2 3 1 4 4 4 2 1 2 4 5 5 4 4 3 3 4 3 5 5 5 4 4 4 4 3 3 3 3 4 4 4 5 5 5 5 4 5 4 4 2 1 1 4 5 1 2 4 1 1 1 5 6 5 4 6 1 5 2 2 1 5 2 2 5 4 4 4.0 5 3 3
175 176 22 0 3 2 2 1 1 1 2 2 3 2 2 1 2 1 1 1 3 5 4 5 5 5 5 5 5 5 5 4 5 4 5 5 5 5 5 5 5 2 2 2 5 5 2 2 5 5 1 2 1 1 1 5 1 1 1 1 1 1 1 1 1 1 1 1 2 4 2 5 2 2 5 5 5 5.0 5 5 5
178 179 22 0 3 4 2 2 1 3 2 3 5 1 2 2 2 1 1 4 1 5 5 5 5 5 5 5 5 5 5 4 4 3 4 4 3 3 3 3 5 5 4 3 5 3 4 4 4 5 3 2 3 4 2 1 4 3 1 1 3 5 5 6 1 4 6 5 2 2 2 4 2 3 4 4 4 4.0 3 4 4
179 180 21 0 3 2 2 1 1 1 3 2 1 1 2 4 2 2 1 1 1 4 4 3 2 4 2 2 4 4 4 4 4 5 4 2 2 2 4 4 4 3 3 3 5 5 5 5 4 4 1 2 1 5 1 1 3 1 1 3 1 1 2 1 4 1 1 1 4 3 5 5 5 5 5 5 4 4.0 5 4 3
180 181 22 1 3 2 1 2 1 3 2 2 3 1 3 4 2 2 1 1 3 5 5 5 4 4 5 4 5 4 5 4 5 5 5 5 5 4 4 5 5 4 3 3 4 3 3 3 5 3 3 3 1 4 5 3 2 3 3 3 3 1 1 1 2 3 3 1 5 5 5 5 2 5 3 5 5 5.0 5 2 2
182 183 22 0 3 4 3 1 2 4 2 3 4 1 2 2 2 1 1 4 4 4 5 5 3 5 5 4 5 3 3 5 5 5 3 3 3 3 5 5 5 5 3 3 5 5 5 4 4 5 5 1 1 5 5 5 5 5 1 1 1 5 5 5 5 5 5 5 4 1 1 5 1 1 5 4 4 5.0 4 5 4
183 184 22 0 3 2 4 2 1 4 2 4 5 1 1 1 2 2 1 4 2 4 4 4 4 4 4 4 4 5 4 5 5 5 4 5 5 5 5 5 4 5 5 5 5 4 5 2 4 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 2 2 4 2 2 4 4 3 4.0 4 5 4
187 188 22 0 3 4 2 2 2 2 4 2 5 1 2 1 2 1 1 5 4 5 5 3 3 5 5 3 3 5 5 5 5 5 5 3 3 3 4 4 5 5 5 5 5 5 5 5 3 4 2 2 1 4 4 4 1 3 2 1 3 5 5 5 1 5 5 5 2 2 2 5 3 5 5 5 5 5.0 5 5 5
192 193 17 1 1 5 4 2 2 1 3 2 3 2 2 3 2 1 1 4 3 5 5 5 4 5 5 2 5 5 5 5 5 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 4 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.0 5 5 4
194 195 17 1 1 5 1 2 2 3 3 2 4 3 3 3 2 1 1 6 3 5 5 4 4 4 4 4 4 5 5 5 5 5 5 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 4 3 3 5 5 5 5 5 5 5.0 5 5 5
201 202 20 0 2 3 1 2 1 1 3 2 3 1 2 1 2 1 1 3 3 4 4 2 2 3 4 2 2 4 4 4 4 4 4 4 2 2 4 4 4 4 4 4 4 4 2 4 4 4 1 1 1 1 2 1 1 1 1 1 1 4 4 4 4 4 4 4 3 3 2 4 2 3 4 4 4 4.0 4 4 4
203 204 19 0 2 4 3 1 2 2 2 1 5 2 2 2 2 1 1 6 5 4 4 1 1 5 5 1 4 4 5 5 5 5 4 3 3 2 5 5 5 4 3 2 2 3 2 4 4 3 1 4 1 4 1 4 3 4 4 1 4 2 1 2 7 2 2 2 5 2 2 4 2 2 5 5 3 5.0 5 2 2
212 213 20 1 2 2 2 1 1 1 1 4 2 1 1 2 2 1 1 2 1 4 2 3 3 5 4 3 5 5 5 4 4 4 4 2 2 2 2 2 2 4 4 5 4 4 5 5 2 5 1 1 1 4 4 4 1 1 1 1 1 3 3 1 1 1 1 1 4 4 3 3 4 4 5 5 5 5.0 5 5 5
217 218 18 1 2 2 1 1 1 1 2 2 2 2 4 4 2 2 1 3 2 4 3 4 3 5 5 4 3 5 5 5 3 4 4 5 5 3 5 5 4 2 2 2 4 2 2 4 4 4 1 3 4 4 1 1 2 1 1 1 1 1 3 4 5 3 6 1 5 3 2 2 2 2 4 5 3 4.0 5 2 1
222 223 18 1 2 3 1 2 2 2 1 2 5 2 1 2 2 2 1 5 5 3 4 4 2 2 4 2 3 4 4 4 3 3 3 3 3 3 2 2 2 2 2 2 2 3 2 2 4 3 1 5 1 4 1 1 5 1 1 3 1 6 2 6 5 1 1 6 3 2 2 4 2 2 4 4 2 4.0 4 2 3
239 240 22 0 3 6 2 1 4 3 4 3 3 2 2 3 2 1 1 1 3 4 4 2 2 4 4 2 2 4 5 3 4 4 3 3 2 2 4 4 4 5 4 3 3 3 4 4 3 4 5 1 1 1 1 1 1 1 1 1 1 1 2 1 5 3 2 2 2 1 1 4 1 1 4 4 2 4.0 4 2 3
241 242 21 1 2 1 1 2 2 5 4 1 3 1 2 2 2 1 1 1 3 4 4 2 2 2 2 2 2 4 4 3 4 3 3 2 2 2 2 2 3 1 1 1 1 3 1 2 3 1 2 5 1 1 1 5 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 4 2 2 4 3 2 4.0 4 2 2
250 251 21 0 3 4 3 2 1 4 2 4 2 1 2 2 2 1 1 2 2 3 3 3 3 4 3 3 4 4 2 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 4 4 5 5 1 5 5 5 5 5 5 5 5 3 3 3 3 5 1 3 4 3 3 3 5 3 5 5 5 5.0 5 5 3
258 259 20 1 2 4 3 2 2 1 3 1 2 2 4 3 2 1 3 4 3 4 4 3 5 5 4 4 3 3 3 3 3 5 5 4 4 3 4 5 5 4 3 3 5 5 4 3 4 4 2 3 2 2 3 4 2 2 3 2 3 5 5 5 3 6 5 5 5 5 5 5 5 5 5 5 5 5.0 5 5 5
262 263 17 1 1 5 2 2 1 1 2 1 1 2 3 4 2 1 1 5 1 5 4 3 2 1 2 3 4 5 4 3 5 4 3 4 4 3 3 5 5 4 3 4 4 3 3 4 4 5 5 5 5 5 4 1 2 2 5 5 5 6 6 6 6 6 6 6 2 2 5 5 5 5 5 5 5 5.0 5 5 5
272 273 20 0 3 2 2 1 2 3 4 1 2 1 3 3 2 1 1 6 2 4 4 3 4 5 5 3 4 3 2 2 3 2 3 2 3 3 4 5 2 1 1 1 5 5 1 3 5 4 5 1 1 1 5 1 1 1 1 1 5 4 5 4 6 2 2 4 3 2 1 5 2 2 5 4 3 5.0 5 5 3
273 274 21 0 3 4 3 2 1 3 3 1 3 1 2 2 2 1 1 5 3 4 4 1 1 4 1 1 4 4 1 4 4 4 4 4 4 1 4 4 4 4 4 4 4 4 4 4 4 4 5 4 5 1 1 1 1 1 1 1 1 6 6 6 6 5 5 6 2 1 1 4 1 1 4 4 4 4.0 4 4 4
276 277 21 0 3 6 3 1 1 4 2 4 2 1 2 3 2 1 1 3 1 4 4 5 5 5 5 5 4 5 2 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 5 1 1 1 1 5 5 4 4 4 4 4 4 4 4 1 1 5 5 5 5 5 5 5.0 5 5 5
282 283 20 0 2 5 2 2 1 2 2 2 1 2 2 3 2 2 1 2 1 4 3 3 3 4 4 2 4 4 3 4 4 4 4 3 4 3 4 4 4 2 2 2 2 4 2 2 4 4 4 4 1 4 5 1 4 1 3 2 4 3 5 6 1 2 5 3 4 4 2 4 2 4 4 4 2 4.0 5 4 3
283 284 19 0 2 4 2 1 1 4 3 3 1 1 2 2 2 2 1 2 1 4 4 2 2 4 4 1 3 5 5 4 4 4 4 2 1 4 3 3 3 2 2 2 5 4 1 1 3 3 5 5 1 2 5 1 1 5 1 1 5 3 5 3 3 1 3 3 2 2 4 5 2 2 5 4 2 3.0 5 1 3
293 294 19 0 2 4 2 1 1 2 1 3 4 2 1 2 2 2 1 1 4 5 4 4 4 5 5 4 4 5 4 5 5 5 4 4 5 4 5 5 5 5 2 2 2 4 2 4 4 4 2 3 4 1 4 4 4 4 4 4 4 1 1 1 1 1 1 1 5 4 2 5 5 4 4 3 3 4.0 5 5 4
294 295 19 0 2 4 1 1 1 1 3 2 1 2 1 1 2 2 1 1 1 5 4 4 4 5 5 3 4 5 5 5 5 5 5 4 4 4 5 5 5 4 1 1 5 5 1 4 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 5 2 5 2 2 5 5 5 5.0 4 4 5
296 297 19 0 2 4 2 1 1 1 3 2 6 2 2 1 2 2 1 1 6 5 4 4 3 5 5 2 4 4 5 4 5 5 4 3 2 2 5 5 5 5 5 4 5 4 5 4 4 5 1 3 5 4 1 1 1 1 1 2 1 5 1 5 4 5 1 1 5 4 2 5 5 5 5 5 5 5.0 5 5 4
302 303 20 1 2 4 1 1 1 1 2 1 3 1 4 4 2 1 1 6 2 5 3 3 3 5 4 3 4 4 4 4 4 4 4 3 4 2 4 4 4 3 3 3 3 4 4 4 4 4 5 5 1 1 5 1 3 1 1 1 1 2 1 1 1 5 5 5 2 2 4 5 5 2 4 4 5 4.0 5 4 3
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499 500 25 1 3 1 1 1 2 1 3 1 1 2 2 2 2 2 1 1 1 4 4 2 3 4 5 2 3 4 5 3 5 4 2 1 2 2 3 3 4 2 2 2 2 4 1 3 5 3 5 3 1 1 5 1 3 1 1 2 1 5 5 5 5 2 1 5 3 4 2 5 1 2 5 3 2 4.0 5 1 3
500 501 21 1 3 2 2 2 1 1 2 3 4 2 3 4 2 2 1 1 4 5 5 5 5 5 5 5 3 5 5 4 4 4 3 3 3 3 3 3 5 2 1 5 4 4 1 1 4 3 5 5 5 5 5 5 5 5 5 5 1 1 1 1 1 1 1 1 5 5 3 5 3 3 5 5 3 5.0 5 5 3
502 503 22 1 3 2 1 1 1 5 2 3 3 1 2 1 2 1 1 4 3 4 4 4 4 5 5 4 4 4 3 4 5 4 5 4 4 4 4 3 4 2 2 4 4 3 3 3 3 4 5 1 1 1 5 1 1 1 1 1 1 2 3 1 1 3 1 1 4 3 3 5 3 3 5 4 4 5.0 5 2 1
503 504 26 1 3 2 1 1 2 1 4 1 2 1 1 1 2 1 1 2 2 3 5 4 2 1 4 5 3 5 4 4 5 2 4 3 5 2 1 4 5 4 2 4 1 5 5 5 4 3 3 1 2 2 1 2 5 2 1 2 3 2 3 3 1 4 2 6 3 5 3 4 3 3 5 2 4 1.0 5 4 2
504 505 21 1 3 6 4 1 1 1 2 2 4 2 3 4 2 1 1 2 4 5 5 5 3 5 4 5 1 5 5 5 5 5 5 5 5 4 5 5 5 5 4 5 5 5 5 5 3 5 5 5 1 1 1 1 1 1 1 1 5 3 3 3 3 3 3 3 5 5 3 5 3 4 5 5 4 5.0 5 4 3
507 508 21 0 3 4 4 2 1 1 3 3 3 2 2 3 2 1 1 4 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 4 5 5 3 5 5 5 5 5 5 5 1 5 1 1 5 1 1 1 1 1 5 5 5 5 5 5 5 5 5 5 3 5 3 4 5 5 4 5.0 5 4 3
509 510 22 1 3 2 4 1 1 4 2 4 4 1 4 3 2 2 1 3 4 4 4 3 2 3 3 1 3 4 4 4 4 4 2 1 1 1 3 3 4 4 4 4 4 4 3 4 4 4 5 5 5 5 5 5 5 5 5 5 5 4 1 4 5 5 1 3 2 2 2 4 1 3 4 5 3 4.0 4 4 2
511 512 21 1 3 4 1 1 1 1 3 1 3 1 2 3 2 1 1 1 3 5 4 5 3 4 3 3 3 5 5 3 4 3 3 3 3 5 3 3 2 2 2 2 3 3 2 2 4 3 5 1 1 1 1 5 5 1 1 1 1 4 1 4 5 1 1 4 4 2 2 4 2 1 4 4 3 5.0 5 2 3
In [34]:
# Checking condition
condition = data1['Frequent Communication Type'] >= 3

# Update values in the specified column based on the condition
data1.loc[condition, 'Frequent Communication Type'] = 2  # Replace 'updated_value' with the value you want to set
In [35]:
data1['Frequent Communication Type'].value_counts()
Out[35]:
1    355
2    172
Name: Frequent Communication Type, dtype: int64
In [36]:
data1[(data1['Feel safe and secure with mobile']>=3)]
Out[36]:
Feature Record_ID Age Gender Education Father Occupation Household Income Native (Rural/Urban) Handset Type(Color/ Black & white) Handset Brand Handset Selection Reason Handset Cost Handset Age Calls Receiving per day SMS Receiving per day SMS Sending per day Frequent Communication Type Feel safe and secure with mobile Monthly Mobile Expenditure Current Service Provider Current Service Provider Age SP - Influence Rating for Signal SP - Influence Rating for Customer care SP - Influence Rating for Electronic media SP - Influence Rating for Hoardings SP - Influence Rating for Friends SP - Influence Rating for Family members SP - Influence Rating for Print media SP - Influence Rating for Call costs HS - Influence Rating for Brand HS - Influence Rating for Battery back up HS - Influence Rating for Keypad HS - Influence Rating for Display HS - Influence Rating for Sound HS - Influence Rating for Size of the screen HS - Influence Rating for Electronic media HS - Influence Rating for Print media HS - Influence Rating for Hoardings HS - Influence Rating for Friends HS - Influence Rating for Family members HS - Influence Rating for Voice clarity HS - Influence Rating for MP3 facility HS - Influence Rating for Bluetooth HS - Influence Rating for Camera pixels HS - Influence Rating for FM radio HS - Influence Rating for Games HS - Influence Rating for Video HS - Influence Rating for Memory size HS - Influence Rating for Price HS - Influence Rating for Stylish Stylish Handset Brand More Models Handset Brand User friendly Handset Brand Reasonable prices Handset Brand Added features Handset Brand Customer service Handset Brand Advertisements Handset Brand Sales promotions Handset Brand Available service centers Handset Brand Durability Handset Brand Overall, I like the Handset Brand User friendly Service Provider Added features Service Provider Customer service Service Provider Advertisements Service Provider Sales promotions Service Provider Available service centers Service Provider Overall, I like Service Provider All my friends use Old fashioned Prestige Secure Show-off Life style Communication Facilities Life easier Call Friends and Family Mostly use SMS Music or Videos Games
249 250 24 1 2 3 1 2 1 3 3 2 2 2 2 1 1 3 1 1 6 5 5 5 5 5 5 5 5 4 5 5 5 5 4 4 5 5 5 5 5 5 5 4 4 4 3 4 4 4 3 4 3 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 5 5 5 5 5 5 5.0 4 5 3
275 276 21 0 4 3 2 1 1 1 3 2 1 1 1 2 1 3 1 1 2 5 4 3 4 4 4 5 4 5 5 5 5 1 4 4 4 4 4 4 4 1 5 1 5 2 1 1 3 2 5 3 1 2 5 1 5 3 1 1 1 4 4 1 5 1 1 1 3 4 2 5 2 2 5 4 5 5.0 4 2 2
480 481 25 1 3 1 1 1 2 1 1 1 1 1 1 1 1 3 1 1 1 5 4 3 2 3 5 2 4 4 5 3 3 3 2 2 2 2 3 5 3 4 3 3 3 3 3 2 3 3 1 1 5 1 5 1 3 1 5 1 1 3 3 1 3 3 3 3 3 3 3 5 3 3 5 3 3 4.0 3 3 3
In [37]:
# Checking condition
condition = data1['Feel safe and secure with mobile'] >= 3

# Update values in the specified column based on the condition
data1.loc[condition, 'Feel safe and secure with mobile'] = 2  # Replace 'updated_value' with the value you want to set
In [38]:
data1['Handset Type(Color/ Black & white)'].value_counts()
Out[38]:
1    405
2    115
5      3
4      2
3      2
Name: Handset Type(Color/ Black & white), dtype: int64
In [39]:
data1[(data1['Handset Type(Color/ Black & white)']>=3)]
Out[39]:
Feature Record_ID Age Gender Education Father Occupation Household Income Native (Rural/Urban) Handset Type(Color/ Black & white) Handset Brand Handset Selection Reason Handset Cost Handset Age Calls Receiving per day SMS Receiving per day SMS Sending per day Frequent Communication Type Feel safe and secure with mobile Monthly Mobile Expenditure Current Service Provider Current Service Provider Age SP - Influence Rating for Signal SP - Influence Rating for Customer care SP - Influence Rating for Electronic media SP - Influence Rating for Hoardings SP - Influence Rating for Friends SP - Influence Rating for Family members SP - Influence Rating for Print media SP - Influence Rating for Call costs HS - Influence Rating for Brand HS - Influence Rating for Battery back up HS - Influence Rating for Keypad HS - Influence Rating for Display HS - Influence Rating for Sound HS - Influence Rating for Size of the screen HS - Influence Rating for Electronic media HS - Influence Rating for Print media HS - Influence Rating for Hoardings HS - Influence Rating for Friends HS - Influence Rating for Family members HS - Influence Rating for Voice clarity HS - Influence Rating for MP3 facility HS - Influence Rating for Bluetooth HS - Influence Rating for Camera pixels HS - Influence Rating for FM radio HS - Influence Rating for Games HS - Influence Rating for Video HS - Influence Rating for Memory size HS - Influence Rating for Price HS - Influence Rating for Stylish Stylish Handset Brand More Models Handset Brand User friendly Handset Brand Reasonable prices Handset Brand Added features Handset Brand Customer service Handset Brand Advertisements Handset Brand Sales promotions Handset Brand Available service centers Handset Brand Durability Handset Brand Overall, I like the Handset Brand User friendly Service Provider Added features Service Provider Customer service Service Provider Advertisements Service Provider Sales promotions Service Provider Available service centers Service Provider Overall, I like Service Provider All my friends use Old fashioned Prestige Secure Show-off Life style Communication Facilities Life easier Call Friends and Family Mostly use SMS Music or Videos Games
58 59 22 1 3 2 1 1 4 1 4 4 2 2 2 3 2 1 2 1 4 5 5 3 4 5 4 3 3 5 4 3 5 3 5 2 4 3 5 4 5 3 2 4 5 3 5 4 3 5 5 1 1 5 2 1 5 4 1 2 3 1 4 3 6 7 2 5 3 4 3 5 4 3 5 4 4 4.0 3 4 4
81 82 25 1 3 2 3 1 3 1 3 1 3 1 2 3 2 1 1 7 1 3 3 3 4 3 4 3 4 3 4 3 2 5 3 2 3 5 4 3 2 1 2 3 3 3 3 4 4 3 5 2 1 1 5 1 2 1 1 3 1 1 4 1 5 1 1 1 2 2 2 4 2 4 4 4 4 4.0 3 3 2
193 194 17 1 1 4 4 2 5 1 1 4 3 2 2 2 1 1 2 4 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 4 4 4 5 5 5 5 5 5 5.0 5 5 5
239 240 22 0 3 6 2 1 4 3 4 3 3 2 2 3 2 1 1 1 3 4 4 2 2 4 4 2 2 4 5 3 4 4 3 3 2 2 4 4 4 5 4 3 3 3 4 4 3 4 5 1 1 1 1 1 1 1 1 1 1 1 2 1 5 3 2 2 2 1 1 4 1 1 4 4 2 4.0 4 2 3
245 246 29 1 2 4 2 2 3 3 4 4 2 2 2 2 1 1 4 4 3 4 4 4 4 4 4 4 4 3 4 4 4 5 4 4 4 3 3 4 4 4 4 3 4 4 3 4 3 4 3 3 3 3 3 3 3 3 3 3 3 5 5 5 5 5 5 5 3 3 5 5 5 5 5 5 5 5.0 3 5 2
394 395 23 1 3 5 3 1 5 2 2 3 2 1 1 1 1 2 1 1 3 5 5 1 1 4 5 1 4 5 5 4 4 4 5 1 1 1 5 5 5 1 1 1 4 4 1 1 4 4 1 1 2 1 1 2 1 1 1 1 2 1 1 1 1 1 1 1 5 4 2 5 5 5 5 5 3 5.0 4 3 3
472 473 23 1 3 2 1 1 5 4 2 3 3 4 4 1 1 2 1 5 6 5 3 2 2 4 4 2 4 5 3 3 3 4 4 3 3 4 4 2 2 2 2 2 2 4 3 3 3 3 1 4 1 1 1 1 5 1 1 1 5 5 5 5 1 7 1 5 4 3 3 3 4 3 4 4 3 5.0 3 4 4
In [40]:
# Checking condition
condition = data1['Handset Type(Color/ Black & white)'] >= 3

# Update values in the specified column based on the condition
data1.loc[condition, 'Handset Type(Color/ Black & white)'] = 2  # Replace 'updated_value' with the value you want to set
In [41]:
data1['Handset Type(Color/ Black & white)'].value_counts()
Out[41]:
1    405
2    122
Name: Handset Type(Color/ Black & white), dtype: int64
In [42]:
grouped_data = data1.groupby('Education')['Education'].count()
In [43]:
print(grouped_data)
Education
1     34
2    139
3    297
4     57
Name: Education, dtype: int64
In [44]:
for column in data1.columns:
    print(f"Value counts for {column}:\n{data1[column].value_counts()}\n")
Value counts for Record_ID:
1      1
363    1
361    1
360    1
359    1
358    1
357    1
356    1
355    1
354    1
353    1
352    1
351    1
350    1
349    1
348    1
347    1
346    1
345    1
344    1
343    1
342    1
341    1
340    1
339    1
338    1
337    1
336    1
335    1
334    1
333    1
362    1
364    1
331    1
365    1
394    1
393    1
392    1
391    1
390    1
389    1
388    1
387    1
386    1
385    1
384    1
383    1
382    1
381    1
380    1
379    1
378    1
377    1
376    1
375    1
374    1
373    1
372    1
371    1
370    1
369    1
368    1
367    1
366    1
332    1
330    1
396    1
297    1
295    1
294    1
293    1
292    1
291    1
290    1
289    1
288    1
287    1
286    1
285    1
284    1
283    1
282    1
281    1
280    1
279    1
278    1
277    1
276    1
275    1
274    1
273    1
272    1
271    1
270    1
269    1
268    1
267    1
296    1
298    1
329    1
299    1
328    1
327    1
326    1
325    1
324    1
323    1
322    1
321    1
320    1
319    1
318    1
317    1
316    1
315    1
314    1
313    1
312    1
311    1
310    1
309    1
308    1
307    1
306    1
305    1
304    1
303    1
302    1
301    1
300    1
395    1
397    1
265    1
495    1
493    1
492    1
491    1
490    1
489    1
488    1
487    1
486    1
485    1
484    1
483    1
482    1
481    1
480    1
479    1
478    1
477    1
476    1
475    1
474    1
473    1
472    1
471    1
470    1
469    1
468    1
467    1
466    1
465    1
494    1
496    1
463    1
497    1
526    1
525    1
524    1
523    1
522    1
521    1
520    1
519    1
518    1
517    1
516    1
515    1
514    1
513    1
512    1
511    1
510    1
509    1
508    1
507    1
506    1
505    1
504    1
503    1
502    1
501    1
500    1
499    1
498    1
464    1
462    1
398    1
429    1
427    1
426    1
425    1
424    1
423    1
422    1
421    1
420    1
419    1
418    1
417    1
416    1
415    1
414    1
413    1
412    1
411    1
410    1
409    1
408    1
407    1
406    1
405    1
404    1
403    1
402    1
401    1
400    1
399    1
428    1
430    1
461    1
431    1
460    1
459    1
458    1
457    1
456    1
455    1
454    1
453    1
452    1
451    1
450    1
449    1
448    1
447    1
446    1
445    1
444    1
443    1
442    1
441    1
440    1
439    1
438    1
437    1
436    1
435    1
434    1
433    1
432    1
266    1
264    1
2      1
99     1
97     1
96     1
95     1
94     1
93     1
92     1
91     1
90     1
89     1
88     1
87     1
86     1
85     1
84     1
83     1
82     1
81     1
80     1
79     1
78     1
77     1
76     1
75     1
74     1
73     1
72     1
71     1
70     1
69     1
98     1
100    1
67     1
101    1
130    1
129    1
128    1
127    1
126    1
125    1
124    1
123    1
122    1
121    1
120    1
119    1
118    1
117    1
116    1
115    1
114    1
113    1
112    1
111    1
110    1
109    1
108    1
107    1
106    1
105    1
104    1
103    1
102    1
68     1
66     1
132    1
33     1
31     1
30     1
29     1
28     1
27     1
26     1
25     1
24     1
23     1
22     1
21     1
20     1
19     1
18     1
17     1
16     1
15     1
14     1
13     1
12     1
11     1
10     1
9      1
8      1
7      1
6      1
5      1
4      1
3      1
32     1
34     1
65     1
35     1
64     1
63     1
62     1
61     1
60     1
59     1
58     1
57     1
56     1
55     1
54     1
53     1
52     1
51     1
50     1
49     1
48     1
47     1
46     1
45     1
44     1
43     1
42     1
41     1
40     1
39     1
38     1
37     1
36     1
131    1
133    1
263    1
231    1
229    1
228    1
227    1
226    1
225    1
224    1
223    1
222    1
221    1
220    1
219    1
218    1
217    1
216    1
215    1
214    1
213    1
212    1
211    1
210    1
209    1
208    1
207    1
206    1
205    1
204    1
203    1
202    1
201    1
230    1
232    1
199    1
233    1
262    1
261    1
260    1
259    1
258    1
257    1
256    1
255    1
254    1
253    1
252    1
251    1
250    1
249    1
248    1
247    1
246    1
245    1
244    1
243    1
242    1
241    1
240    1
239    1
238    1
237    1
236    1
235    1
234    1
200    1
198    1
134    1
165    1
163    1
162    1
161    1
160    1
159    1
158    1
157    1
156    1
155    1
154    1
153    1
152    1
151    1
150    1
149    1
148    1
147    1
146    1
145    1
144    1
143    1
142    1
141    1
140    1
139    1
138    1
137    1
136    1
135    1
164    1
166    1
197    1
167    1
196    1
195    1
194    1
193    1
192    1
191    1
190    1
189    1
188    1
187    1
186    1
185    1
184    1
183    1
182    1
181    1
180    1
179    1
178    1
177    1
176    1
175    1
174    1
173    1
172    1
171    1
170    1
169    1
168    1
527    1
Name: Record_ID, dtype: int64

Value counts for Age:
22    100
21     99
23     51
20     49
19     44
24     34
18     33
26     28
25     24
17     19
29     13
27     12
28     10
30      7
16      3
33      1
Name: Age, dtype: int64

Value counts for Gender:
1    379
0    148
Name: Gender, dtype: int64

Value counts for Education:
3    297
2    139
4     57
1     34
Name: Education, dtype: int64

Value counts for Father Occupation:
2    204
4    162
5     63
3     38
1     37
6     23
Name: Father Occupation, dtype: int64

Value counts for Household Income:
2    177
1    171
3    111
4     68
Name: Household Income, dtype: int64

Value counts for Native (Rural/Urban):
1    346
2    181
Name: Native (Rural/Urban), dtype: int64

Value counts for Handset Type(Color/ Black & white):
1    405
2    122
Name: Handset Type(Color/ Black & white), dtype: int64

Value counts for Handset Brand:
1    303
2     67
3     66
4     55
5     36
Name: Handset Brand, dtype: int64

Value counts for Handset Selection Reason:
2    214
3    131
1    109
4     73
Name: Handset Selection Reason, dtype: int64

Value counts for Handset Cost:
2    224
3    144
1     89
4     70
Name: Handset Cost, dtype: int64

Value counts for Handset Age:
3    172
2    104
1    101
4     84
6     33
5     33
Name: Handset Age, dtype: int64

Value counts for Calls Receiving per day:
1    298
2    182
3     36
4     11
Name: Calls Receiving per day, dtype: int64

Value counts for SMS Receiving per day:
1    257
2    178
3     56
4     36
Name: SMS Receiving per day, dtype: int64

Value counts for SMS Sending per day:
1    278
2    131
3     65
4     53
Name: SMS Sending per day, dtype: int64

Value counts for Frequent Communication Type:
1    355
2    172
Name: Frequent Communication Type, dtype: int64

Value counts for Feel safe and secure with mobile:
1    310
2    217
Name: Feel safe and secure with mobile, dtype: int64

Value counts for Monthly Mobile Expenditure:
1    371
2    113
3     25
4     18
Name: Monthly Mobile Expenditure, dtype: int64

Value counts for Current Service Provider:
1    176
2     95
4     90
3     83
5     46
6     35
7      2
Name: Current Service Provider, dtype: int64

Value counts for Current Service Provider Age:
3    175
2    105
1     89
4     75
6     51
5     32
Name: Current Service Provider Age, dtype: int64

Value counts for SP - Influence Rating for Signal:
5    250
4    215
3     46
2     12
1      4
Name: SP - Influence Rating for Signal, dtype: int64

Value counts for SP - Influence Rating for Customer care:
4    257
5    151
3     75
2     37
1      7
Name: SP - Influence Rating for Customer care, dtype: int64

Value counts for SP - Influence Rating for Electronic media:
4    179
3    152
5     82
2     79
1     35
Name: SP - Influence Rating for Electronic media, dtype: int64

Value counts for SP - Influence Rating for Hoardings:
3    159
4    158
2     97
5     70
1     43
Name: SP - Influence Rating for Hoardings, dtype: int64

Value counts for SP - Influence Rating for Friends:
4    230
5    162
3     92
2     35
1      8
Name: SP - Influence Rating for Friends, dtype: int64

Value counts for SP - Influence Rating for Family members:
4    232
5    168
3     81
2     37
1      9
Name: SP - Influence Rating for Family members, dtype: int64

Value counts for SP - Influence Rating for Print media:
3    168
4    142
2    109
5     65
1     43
Name: SP - Influence Rating for Print media, dtype: int64

Value counts for SP - Influence Rating for Call costs:
4    217
5    131
3    111
2     50
1     18
Name: SP - Influence Rating for Call costs, dtype: int64

Value counts for HS - Influence Rating for Brand:
5    268
4    211
3     40
1      6
2      2
Name: HS - Influence Rating for Brand, dtype: int64

Value counts for HS - Influence Rating for Battery back up:
5    228
4    220
3     56
2     20
1      3
Name: HS - Influence Rating for Battery back up, dtype: int64

Value counts for HS - Influence Rating for Keypad:
4    242
5    173
3     93
2     14
1      5
Name: HS - Influence Rating for Keypad, dtype: int64

Value counts for HS - Influence Rating for Display:
4    229
5    207
3     67
2     19
1      5
Name: HS - Influence Rating for Display, dtype: int64

Value counts for HS - Influence Rating for Sound:
4    227
5    197
3     79
2     19
1      5
Name: HS - Influence Rating for Sound, dtype: int64

Value counts for HS - Influence Rating for Size of the screen:
4    237
5    131
3    120
2     25
1     14
Name: HS - Influence Rating for Size of the screen, dtype: int64

Value counts for HS - Influence Rating for Electronic media:
4    166
3    165
2     82
5     80
1     34
Name: HS - Influence Rating for Electronic media, dtype: int64

Value counts for HS - Influence Rating for Print media:
3    175
4    153
2     88
5     70
1     41
Name: HS - Influence Rating for Print media, dtype: int64

Value counts for HS - Influence Rating for Hoardings:
3    172
4    159
2     91
5     62
1     43
Name: HS - Influence Rating for Hoardings, dtype: int64

Value counts for HS - Influence Rating for Friends:
4    241
5    141
3    101
2     31
1     13
Name: HS - Influence Rating for Friends, dtype: int64

Value counts for HS - Influence Rating for Family members:
4    243
5    154
3     88
2     33
1      9
Name: HS - Influence Rating for Family members, dtype: int64

Value counts for HS - Influence Rating for Voice clarity:
4    236
5    169
3     81
2     30
1     11
Name: HS - Influence Rating for Voice clarity, dtype: int64

Value counts for HS - Influence Rating for MP3 facility:
4    139
2    118
5    108
3     97
1     65
Name: HS - Influence Rating for MP3 facility, dtype: int64

Value counts for HS - Influence Rating for Bluetooth:
2    123
4    118
3    110
5    104
1     72
Name: HS - Influence Rating for Bluetooth, dtype: int64

Value counts for HS - Influence Rating for Camera pixels:
4    132
2    122
5    103
3    101
1     69
Name: HS - Influence Rating for Camera pixels, dtype: int64

Value counts for HS - Influence Rating for FM radio:
4    179
5    162
2     75
3     69
1     42
Name: HS - Influence Rating for FM radio, dtype: int64

Value counts for HS - Influence Rating for Games:
4    196
3    126
5    119
2     61
1     25
Name: HS - Influence Rating for Games, dtype: int64

Value counts for HS - Influence Rating for Video:
4    146
2    111
3    102
5    101
1     67
Name: HS - Influence Rating for Video, dtype: int64

Value counts for HS - Influence Rating for Memory size:
4    182
3    114
5    113
2     70
1     48
Name: HS - Influence Rating for Memory size, dtype: int64

Value counts for HS - Influence Rating for Price:
4    241
5    137
3    104
2     32
1     13
Name: HS - Influence Rating for Price, dtype: int64

Value counts for HS - Influence Rating for Stylish:
4    205
5    159
3    103
2     41
1     19
Name: HS - Influence Rating for Stylish, dtype: int64

Value counts for Stylish Handset Brand:
1    231
5    162
3     68
2     42
4     24
Name: Stylish Handset Brand, dtype: int64

Value counts for More Models Handset Brand:
1    288
5    115
3     62
2     38
4     24
Name: More Models Handset Brand, dtype: int64

Value counts for User friendly Handset Brand:
1    372
5     56
3     35
2     35
4     29
Name: User friendly Handset Brand, dtype: int64

Value counts for Reasonable prices Handset Brand:
1    321
2     65
4     56
5     43
3     42
Name: Reasonable prices Handset Brand, dtype: int64

Value counts for Added features Handset Brand:
1    295
5    112
3     46
2     44
4     30
Name: Added features Handset Brand, dtype: int64

Value counts for Customer service Handset Brand:
1    390
4     38
5     37
2     31
3     31
Name: Customer service Handset Brand, dtype: int64

Value counts for Advertisements Handset Brand:
1    321
3     65
5     60
2     50
4     31
Name: Advertisements Handset Brand, dtype: int64

Value counts for Sales promotions Handset Brand:
1    363
2     43
3     41
5     41
4     39
Name: Sales promotions Handset Brand, dtype: int64

Value counts for Available service centers Handset Brand:
1    404
5     34
3     34
2     29
4     26
Name: Available service centers Handset Brand, dtype: int64

Value counts for Durability Handset Brand:
1    376
5     49
3     41
2     39
4     22
Name: Durability Handset Brand, dtype: int64

Value counts for Overall, I like the Handset Brand:
1    341
5     97
3     42
4     24
2     23
Name: Overall, I like the Handset Brand, dtype: int64

Value counts for User friendly Service Provider:
1    207
5     79
3     76
4     68
2     54
6     34
7      9
Name: User friendly Service Provider, dtype: int64

Value counts for Added features Service Provider:
1    194
5     94
3     88
4     62
2     46
6     29
7     14
Name: Added features Service Provider, dtype: int64

Value counts for Customer service Service Provider:
1    218
4     78
3     76
5     71
2     40
6     37
7      7
Name: Customer service Service Provider, dtype: int64

Value counts for Advertisements Service Provider:
1    210
4     91
5     85
3     71
6     32
2     31
7      7
Name: Advertisements Service Provider, dtype: int64

Value counts for Sales promotions Service Provider:
1    202
3     94
5     76
4     64
6     42
2     42
7      7
Name: Sales promotions Service Provider, dtype: int64

Value counts for Available service centers Service Provider:
1    236
3     78
5     72
6     51
4     49
2     33
7      8
Name: Available service centers Service Provider, dtype: int64

Value counts for Overall, I like Service Provider:
1    205
3     84
4     74
5     70
6     45
2     35
7     14
Name: Overall, I like Service Provider, dtype: int64

Value counts for All my friends use:
4    148
2    124
3    121
5     95
1     39
Name: All my friends use, dtype: int64

Value counts for Old fashioned:
2    178
3    138
4    122
1     54
5     35
Name: Old fashioned, dtype: int64

Value counts for Prestige:
2    214
3    122
1     81
4     71
5     39
Name: Prestige, dtype: int64

Value counts for Secure:
5    283
4    182
3     48
1      7
2      7
Name: Secure, dtype: int64

Value counts for Show-off:
2    171
3    125
4     86
5     73
1     72
Name: Show-off, dtype: int64

Value counts for Life style:
4    142
2    134
3    120
5     82
1     49
Name: Life style, dtype: int64

Value counts for Communication:
5    304
4    169
3     39
2      8
1      7
Name: Communication, dtype: int64

Value counts for Facilities:
4    203
5    188
3     85
2     37
1     14
Name: Facilities, dtype: int64

Value counts for Life easier:
4    194
5    122
3    122
2     66
1     23
Name: Life easier, dtype: int64

Value counts for Call Friends and Family:
5.0    260
4.0    187
3.0     56
2.0     16
1.0      7
Name: Call Friends and Family, dtype: int64

Value counts for Mostly use SMS:
4    161
5    161
3    120
2     62
1     23
Name: Mostly use SMS, dtype: int64

Value counts for Music or Videos:
4    154
5    118
3    111
2    100
1     44
Name: Music or Videos, dtype: int64

Value counts for Games:
3    152
4    123
2    112
5     88
1     52
Name: Games, dtype: int64

In [45]:
# Mapping dictionary for reverse coding

#Educational Qualification - 1) Intermediate/equivalent 2)U.G.   3)P.G. 4)Research scholars

Gender_Code = {
    1: 'Male',
    0: 'Female'
}

Edu_Code = {
    1: '1-Intermediate/equivalent',
    2: '2-U.G.',
    3: '3-P.G.',
    4: '4-Research scholars'
}

#Father occupation - 1) Coolie 2)Farmer 3)Self- Employed 4)Employee 5)Business 6)Others

Parent_Occu_Code = {
    1: '1-Coolie',
    2: '2-Farmer',
    3: '3-Self-Employed',
    4: '4-Employee',
    5: '5-Business',
    6: '6-Others'
}

#Household income per month - 1)<Rs.5000    2)Rs.5000-10000     3)Rs.10000-15000 4)>15000

Household_Income_Code = {
    1: '1-<Rs.5000',
    2: '2-Rs.5000-10000',
    3: '3-Rs.10000-15000',
    4: '4->15000'
}

#Native (Rural/Urban)	Where are you from?(Rural/Urban)


Nativity_Code = {
    1: 'Rural',
    2: 'Urban'
}

#Which brand of handset are you using currently? - 1)Nokia 2)L.G. 3)Samsung 4)Sony Ericsson  5)Motorola

Handset_Brand_Code = {
    1: 'Nokia',
    2: 'L.G.',
    3: 'Samsung',
    4: 'Sony Ericsson',
    5: 'Motorola'
}

#What made you to select this handset? - 1)Brand reputation 2)Satisfactory features  3)Servicing facilities    4)Price 

Handset_Selection_Code = {
    1: 'Brand reputation',
    2: 'Satisfactory features',
    3: 'Servicing facilities',
    4: 'Price'
}

#How much price does your handset costs ? - 1)<Rs.1500   2)Rs.1500-2500      3)Rs.2500-5000       4)>Rs.5000

Handset_Cost_Code = {
    1: '1-<Rs.1500',
    2: '2-Rs.1500-2500',
    3: '3-Rs.2500-5000',
    4: '4->Rs.5000'
}

#Since how long you have been using this handset? - 
# 1)0-6 months  2)6-12 months  3)1-2 years  4)2-3 years  5)3-4 years  6)>4 years 

Handset_Age_Code = {
    1: '1: 0-6 months',
    2: '2: 6-12 months',
    3: '3: 1-2 years',
    4: '4: 2-3 years',
    5: '5: 3-4 years',
    6: '6: >4 years'
}

#Calls Receiving per day	How many calls you receive per a day? - 1)<10 2)10-25 3)25-50 4)>50
Calls_Rec_Code = {
    1: '1: <10',
    2: '2: 10-25',
    3: '3: 25-50',
    4: '4: >50'
}

#SMS Receiving per day	How many SMS you receive per a day? - 1)<10 2)10-25 3)25-50 4)>50
SMS_Rec_Code = {
    1: '1: <10',
    2: '2: 10-25',
    3: '3: 25-50',
    4: '4: >50'
}

#SMS Sending per day	How many SMS you send per a day? - 1)<10 2)10-25 3)25-50 4)>50
SMS_Send_Code = {
    1: '1: <10',
    2: '2: 10-25',
    3: '3: 25-50',
    4: '4: >50'
}

#Monthly Mobile Expenditure - How much you are spending towards mobile per month? - 
# 1)<Rs.300 2)Rs.300-500 3)Rs.500-1000 4)>Rs.1000

Mobile_Expend_Code = {
    1: '1-<Rs.300',
    2: '2-Rs.300-500',
    3: '3-Rs.500-1000',
    4: '4->Rs.1000'
}

#Current Service Provider - Which service provider do you use currently? - 
# 1)Airtel     2)BSNL     3)Idea     4)Vodafone 5)Reliance    6)Tata    7)Virgin

Service_Provider_Code = {
    1: 'Airtel',
    2: 'BSNL',
    3: 'Idea',
    4: 'Vodafone',
    5: 'Reliance',
    6: 'Tata',
    7: 'Virgin'
}

#Current Service Provider Age	How long you have been using this service provider? - 
# 1)0-6 months  2)6-12 months  3)1-2 years  4)2-3 years  5)3-4 years  6)>4 years 

Service_Provider_Age_Code = {
    1: '1: 0-6 months',
    2: '2: 6-12 months',
    3: '3: 1-2 years',
    4: '4: 2-3 years',
    5: '5: 3-4 years',
    6: '6: >4 years'
}
In [46]:
type(Edu_Code)
Out[46]:
dict
In [47]:
Edu_Code
Out[47]:
{1: '1-Intermediate/equivalent',
 2: '2-U.G.',
 3: '3-P.G.',
 4: '4-Research scholars'}
In [48]:
data1['Education'].map(Edu_Code)
Out[48]:
0                         2-U.G.
1                         3-P.G.
2                         3-P.G.
3                         3-P.G.
4                         3-P.G.
5                         3-P.G.
6                         3-P.G.
7                         3-P.G.
8                         3-P.G.
9                         2-U.G.
10                        3-P.G.
11                        2-U.G.
12                        3-P.G.
13                        3-P.G.
14                        3-P.G.
15                        3-P.G.
16                        3-P.G.
17                        3-P.G.
18                        3-P.G.
19                        3-P.G.
20                        3-P.G.
21                        3-P.G.
22                        2-U.G.
23                        3-P.G.
24                        3-P.G.
25                        3-P.G.
26                        2-U.G.
27                        3-P.G.
28                        2-U.G.
29                        3-P.G.
30                        3-P.G.
31                        3-P.G.
32                        3-P.G.
33                        3-P.G.
34                        3-P.G.
35                        3-P.G.
36                        3-P.G.
37           4-Research scholars
38                        3-P.G.
39                        3-P.G.
40                        3-P.G.
41                        3-P.G.
42                        3-P.G.
43                        2-U.G.
44                        3-P.G.
45                        3-P.G.
46                        3-P.G.
47     1-Intermediate/equivalent
48                        3-P.G.
49                        3-P.G.
50                        3-P.G.
51                        3-P.G.
52                        3-P.G.
53                        3-P.G.
54                        3-P.G.
55                        3-P.G.
56                        3-P.G.
57           4-Research scholars
58                        3-P.G.
59                        3-P.G.
60                        3-P.G.
61                        3-P.G.
62                        2-U.G.
63                        3-P.G.
64                        2-U.G.
65                        3-P.G.
66                        3-P.G.
67                        3-P.G.
68                        3-P.G.
69                        3-P.G.
70                        3-P.G.
71                        2-U.G.
72                        3-P.G.
73                        3-P.G.
74                        3-P.G.
75                        3-P.G.
76                        3-P.G.
77                        3-P.G.
78                        3-P.G.
79                        3-P.G.
80                        3-P.G.
81                        3-P.G.
82                        3-P.G.
83                        3-P.G.
84                        3-P.G.
85                        3-P.G.
86                        2-U.G.
87                        3-P.G.
88           4-Research scholars
89           4-Research scholars
90                        3-P.G.
91           4-Research scholars
92                        3-P.G.
93           4-Research scholars
94           4-Research scholars
95           4-Research scholars
96           4-Research scholars
97           4-Research scholars
98                        3-P.G.
99           4-Research scholars
100          4-Research scholars
101          4-Research scholars
102          4-Research scholars
103          4-Research scholars
104          4-Research scholars
105          4-Research scholars
106          4-Research scholars
107          4-Research scholars
108          4-Research scholars
109          4-Research scholars
110          4-Research scholars
111          4-Research scholars
112                       3-P.G.
113          4-Research scholars
114          4-Research scholars
115          4-Research scholars
116          4-Research scholars
117          4-Research scholars
118                       3-P.G.
119          4-Research scholars
120          4-Research scholars
121          4-Research scholars
122          4-Research scholars
123          4-Research scholars
124          4-Research scholars
125          4-Research scholars
126          4-Research scholars
127          4-Research scholars
128          4-Research scholars
129          4-Research scholars
130          4-Research scholars
131          4-Research scholars
132          4-Research scholars
133          4-Research scholars
134          4-Research scholars
135                       3-P.G.
136          4-Research scholars
137          4-Research scholars
138          4-Research scholars
139          4-Research scholars
140          4-Research scholars
141          4-Research scholars
142          4-Research scholars
143                       3-P.G.
144                       3-P.G.
145                       3-P.G.
146                       2-U.G.
147                       3-P.G.
148                       3-P.G.
149                       3-P.G.
150                       3-P.G.
151                       3-P.G.
152                       3-P.G.
153                       3-P.G.
154                       3-P.G.
155                       3-P.G.
156                       3-P.G.
157                       3-P.G.
158                       3-P.G.
159                       3-P.G.
160                       3-P.G.
161                       3-P.G.
162                       3-P.G.
163                       3-P.G.
164    1-Intermediate/equivalent
165                       3-P.G.
166                       3-P.G.
167                       3-P.G.
168                       3-P.G.
169                       3-P.G.
170                       3-P.G.
171                       3-P.G.
172                       3-P.G.
173                       3-P.G.
174                       3-P.G.
175                       3-P.G.
176                       3-P.G.
177                       3-P.G.
178                       3-P.G.
179                       3-P.G.
180                       3-P.G.
181                       3-P.G.
182                       3-P.G.
183                       3-P.G.
184                       3-P.G.
185                       3-P.G.
186                       3-P.G.
187                       3-P.G.
188    1-Intermediate/equivalent
189    1-Intermediate/equivalent
190    1-Intermediate/equivalent
191    1-Intermediate/equivalent
192    1-Intermediate/equivalent
193    1-Intermediate/equivalent
194    1-Intermediate/equivalent
195    1-Intermediate/equivalent
196    1-Intermediate/equivalent
197    1-Intermediate/equivalent
198    1-Intermediate/equivalent
199    1-Intermediate/equivalent
200                       2-U.G.
201                       2-U.G.
202                       2-U.G.
203                       2-U.G.
204                       2-U.G.
205    1-Intermediate/equivalent
206                       3-P.G.
207                       2-U.G.
208    1-Intermediate/equivalent
209    1-Intermediate/equivalent
210                       2-U.G.
211                       2-U.G.
212                       2-U.G.
213                       2-U.G.
214                       2-U.G.
215                       2-U.G.
216                       2-U.G.
217                       2-U.G.
218                       2-U.G.
219                       3-P.G.
220                       2-U.G.
221                       2-U.G.
222                       2-U.G.
223                       2-U.G.
224                       2-U.G.
225                       2-U.G.
226                       2-U.G.
227                       2-U.G.
228                       2-U.G.
229                       2-U.G.
230                       2-U.G.
231                       2-U.G.
232                       2-U.G.
233                       2-U.G.
234                       2-U.G.
235                       2-U.G.
236                       2-U.G.
237                       2-U.G.
238                       2-U.G.
239                       3-P.G.
240    1-Intermediate/equivalent
241                       2-U.G.
242                       2-U.G.
243                       3-P.G.
244                       3-P.G.
245                       2-U.G.
246                       3-P.G.
247                       3-P.G.
248                       3-P.G.
249                       2-U.G.
250                       3-P.G.
251                       2-U.G.
252                       3-P.G.
253                       2-U.G.
254                       2-U.G.
255                       2-U.G.
256                       2-U.G.
257                       2-U.G.
258                       2-U.G.
259                       3-P.G.
260                       3-P.G.
261                       3-P.G.
262    1-Intermediate/equivalent
263                       3-P.G.
264                       3-P.G.
265                       3-P.G.
266                       3-P.G.
267                       3-P.G.
268                       3-P.G.
269                       3-P.G.
270                       3-P.G.
271                       3-P.G.
272                       3-P.G.
273                       3-P.G.
274                       3-P.G.
275          4-Research scholars
276                       3-P.G.
277                       3-P.G.
278                       2-U.G.
279                       2-U.G.
280                       2-U.G.
281                       2-U.G.
282                       2-U.G.
283                       2-U.G.
284                       2-U.G.
285                       2-U.G.
286                       2-U.G.
287                       2-U.G.
288                       2-U.G.
289                       2-U.G.
290                       2-U.G.
291                       2-U.G.
292                       2-U.G.
293                       2-U.G.
294                       2-U.G.
295                       2-U.G.
296                       2-U.G.
297                       2-U.G.
298                       2-U.G.
299                       2-U.G.
300                       2-U.G.
301                       2-U.G.
302                       2-U.G.
303                       2-U.G.
304                       2-U.G.
305                       2-U.G.
306    1-Intermediate/equivalent
307                       2-U.G.
308                       2-U.G.
309                       2-U.G.
310                       2-U.G.
311                       2-U.G.
312                       2-U.G.
313                       2-U.G.
314                       2-U.G.
315                       2-U.G.
316                       2-U.G.
317                       3-P.G.
318                       3-P.G.
319                       3-P.G.
320                       3-P.G.
321                       3-P.G.
322                       3-P.G.
323                       3-P.G.
324                       2-U.G.
325                       3-P.G.
326    1-Intermediate/equivalent
327                       3-P.G.
328                       3-P.G.
329    1-Intermediate/equivalent
330                       2-U.G.
331    1-Intermediate/equivalent
332    1-Intermediate/equivalent
333    1-Intermediate/equivalent
334                       2-U.G.
335                       2-U.G.
336    1-Intermediate/equivalent
337                       2-U.G.
338                       2-U.G.
339    1-Intermediate/equivalent
340                       2-U.G.
341                       2-U.G.
342                       2-U.G.
343                       2-U.G.
344                       2-U.G.
345                       2-U.G.
346                       2-U.G.
347                       2-U.G.
348                       2-U.G.
349                       2-U.G.
350          4-Research scholars
351          4-Research scholars
352                       3-P.G.
353                       3-P.G.
354                       3-P.G.
355                       3-P.G.
356                       3-P.G.
357                       3-P.G.
358                       3-P.G.
359                       3-P.G.
360                       3-P.G.
361                       3-P.G.
362                       2-U.G.
363                       3-P.G.
364                       3-P.G.
365                       3-P.G.
366                       2-U.G.
367                       2-U.G.
368                       2-U.G.
369                       2-U.G.
370                       2-U.G.
371                       2-U.G.
372                       2-U.G.
373                       2-U.G.
374                       2-U.G.
375                       2-U.G.
376                       2-U.G.
377                       2-U.G.
378                       2-U.G.
379                       2-U.G.
380                       2-U.G.
381                       2-U.G.
382                       2-U.G.
383                       2-U.G.
384                       2-U.G.
385                       2-U.G.
386                       2-U.G.
387    1-Intermediate/equivalent
388                       3-P.G.
389                       3-P.G.
390                       3-P.G.
391                       3-P.G.
392                       3-P.G.
393                       3-P.G.
394                       3-P.G.
395                       3-P.G.
396                       3-P.G.
397                       3-P.G.
398                       3-P.G.
399                       3-P.G.
400    1-Intermediate/equivalent
401                       3-P.G.
402                       3-P.G.
403                       2-U.G.
404    1-Intermediate/equivalent
405    1-Intermediate/equivalent
406    1-Intermediate/equivalent
407                       3-P.G.
408                       2-U.G.
409                       2-U.G.
410                       3-P.G.
411          4-Research scholars
412                       3-P.G.
413                       3-P.G.
414                       3-P.G.
415                       3-P.G.
416                       3-P.G.
417                       3-P.G.
418                       3-P.G.
419                       3-P.G.
420                       3-P.G.
421                       3-P.G.
422                       3-P.G.
423                       3-P.G.
424                       3-P.G.
425                       3-P.G.
426                       3-P.G.
427                       3-P.G.
428                       3-P.G.
429                       3-P.G.
430                       3-P.G.
431                       3-P.G.
432                       3-P.G.
433                       3-P.G.
434                       3-P.G.
435                       3-P.G.
436                       3-P.G.
437                       3-P.G.
438                       3-P.G.
439                       3-P.G.
440                       3-P.G.
441                       3-P.G.
442                       3-P.G.
443                       3-P.G.
444                       3-P.G.
445                       3-P.G.
446                       3-P.G.
447                       3-P.G.
448                       3-P.G.
449                       3-P.G.
450    1-Intermediate/equivalent
451                       3-P.G.
452                       3-P.G.
453                       3-P.G.
454                       3-P.G.
455                       3-P.G.
456                       3-P.G.
457                       3-P.G.
458                       3-P.G.
459                       3-P.G.
460                       3-P.G.
461                       3-P.G.
462                       3-P.G.
463                       3-P.G.
464                       3-P.G.
465                       3-P.G.
466                       3-P.G.
467                       3-P.G.
468                       3-P.G.
469                       3-P.G.
470                       3-P.G.
471                       3-P.G.
472                       3-P.G.
473                       3-P.G.
474                       3-P.G.
475                       3-P.G.
476                       3-P.G.
477                       3-P.G.
478                       3-P.G.
479                       3-P.G.
480                       3-P.G.
481                       3-P.G.
482                       3-P.G.
483                       3-P.G.
484                       3-P.G.
485                       3-P.G.
486                       3-P.G.
487    1-Intermediate/equivalent
488                       3-P.G.
489                       3-P.G.
490                       3-P.G.
491                       3-P.G.
492                       3-P.G.
493                       3-P.G.
494                       3-P.G.
495                       3-P.G.
496                       3-P.G.
497                       3-P.G.
498                       3-P.G.
499                       3-P.G.
500                       3-P.G.
501                       3-P.G.
502                       3-P.G.
503                       3-P.G.
504                       3-P.G.
505                       3-P.G.
506                       3-P.G.
507                       3-P.G.
508                       3-P.G.
509                       3-P.G.
510                       3-P.G.
511                       3-P.G.
512                       3-P.G.
513                       3-P.G.
514                       3-P.G.
515          4-Research scholars
516          4-Research scholars
517                       2-U.G.
518                       3-P.G.
519                       3-P.G.
520                       2-U.G.
521                       3-P.G.
522                       2-U.G.
523                       3-P.G.
524                       3-P.G.
525                       3-P.G.
526                       3-P.G.
Name: Education, dtype: object
In [49]:
data1['Education'].map(Edu_Code).value_counts()
Out[49]:
3-P.G.                       297
2-U.G.                       139
4-Research scholars           57
1-Intermediate/equivalent     34
Name: Education, dtype: int64
In [50]:
#Creating a new dataframe to reverse code some features
data2 = data1.copy()  # Create a copy of the original DataFrame
data2
Out[50]:
Feature Record_ID Age Gender Education Father Occupation Household Income Native (Rural/Urban) Handset Type(Color/ Black & white) Handset Brand Handset Selection Reason Handset Cost Handset Age Calls Receiving per day SMS Receiving per day SMS Sending per day Frequent Communication Type Feel safe and secure with mobile Monthly Mobile Expenditure Current Service Provider Current Service Provider Age SP - Influence Rating for Signal SP - Influence Rating for Customer care SP - Influence Rating for Electronic media SP - Influence Rating for Hoardings SP - Influence Rating for Friends SP - Influence Rating for Family members SP - Influence Rating for Print media SP - Influence Rating for Call costs HS - Influence Rating for Brand HS - Influence Rating for Battery back up HS - Influence Rating for Keypad HS - Influence Rating for Display HS - Influence Rating for Sound HS - Influence Rating for Size of the screen HS - Influence Rating for Electronic media HS - Influence Rating for Print media HS - Influence Rating for Hoardings HS - Influence Rating for Friends HS - Influence Rating for Family members HS - Influence Rating for Voice clarity HS - Influence Rating for MP3 facility HS - Influence Rating for Bluetooth HS - Influence Rating for Camera pixels HS - Influence Rating for FM radio HS - Influence Rating for Games HS - Influence Rating for Video HS - Influence Rating for Memory size HS - Influence Rating for Price HS - Influence Rating for Stylish Stylish Handset Brand More Models Handset Brand User friendly Handset Brand Reasonable prices Handset Brand Added features Handset Brand Customer service Handset Brand Advertisements Handset Brand Sales promotions Handset Brand Available service centers Handset Brand Durability Handset Brand Overall, I like the Handset Brand User friendly Service Provider Added features Service Provider Customer service Service Provider Advertisements Service Provider Sales promotions Service Provider Available service centers Service Provider Overall, I like Service Provider All my friends use Old fashioned Prestige Secure Show-off Life style Communication Facilities Life easier Call Friends and Family Mostly use SMS Music or Videos Games
0 1 21 1 2 2 2 1 1 2 2 2 2 1 2 2 1 1 1 5 4 4 4 3 2 5 4 2 5 5 4 4 4 3 3 3 2 2 4 4 5 3 3 3 4 4 2 4 5 4 1 1 4 3 1 1 1 1 1 1 1 1 1 1 4 5 1 1 3 2 2 4 3 4 4 4 4 4.0 2 3 2
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13 14 22 1 3 4 1 2 1 1 1 2 5 2 2 4 2 1 1 4 3 4 3 4 4 5 5 4 4 5 5 5 5 5 4 4 4 4 5 5 4 4 3 4 4 3 3 4 3 3 1 1 1 1 5 1 1 1 1 1 1 1 3 1 5 6 5 5 4 3 2 5 2 2 5 4 3 3.0 3 2 1
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38 39 24 1 3 2 2 2 1 4 4 2 3 3 3 4 2 1 4 4 4 5 5 4 4 3 3 2 2 5 4 3 5 5 5 4 4 3 3 4 4 5 5 4 4 5 4 3 4 5 3 4 5 5 5 5 3 1 2 3 4 1 2 3 4 1 7 7 4 4 4 3 2 1 1 1 1 2.0 3 4 5
39 40 25 1 3 4 3 1 2 1 2 1 3 1 1 1 1 1 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 4 4 4 2 2 1 3 3 3 4 4 5 5 1 1 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 3 3 3 3 3 3 3 3 3 3.0 3 3 3
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41 42 23 1 3 2 1 1 1 3 2 3 2 1 2 2 2 1 2 2 3 4 1 3 3 3 3 1 3 5 3 2 3 2 2 3 3 3 2 4 2 2 3 2 1 4 3 1 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 3 3 3 3 3 3 3 3 3.0 3 3 3
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56 57 23 1 3 2 1 1 1 1 3 3 1 2 2 2 1 1 4 5 3 5 5 3 4 5 5 4 4 5 4 5 4 4 3 5 4 3 5 4 5 3 5 4 4 3 4 4 4 5 1 2 3 2 3 1 2 1 3 1 3 5 4 3 1 6 7 1 5 5 5 4 5 4 5 5 4 5.0 5 4 5
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69 70 22 1 3 2 3 1 1 1 2 3 4 3 3 4 2 2 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 1 5 5 5 5 1 1 1 1 1 5 5 5 5 5 5 5 4 2 1 4 4 2 4 4 4 4.0 4 4 2
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71 72 23 1 2 2 1 1 1 1 2 2 3 1 3 1 2 2 1 1 5 4 5 3 3 4 3 3 2 4 5 4 4 4 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 1 1 1 3 1 1 1 1 1 1 1 1 5 1 4 1 1 1 4 2 2 4 2 3 4 3 4 5.0 4 3 2
72 73 24 1 3 3 1 1 1 2 4 2 1 1 2 1 1 2 1 5 6 5 3 4 4 2 5 2 5 5 5 5 5 5 4 3 3 3 4 4 5 3 3 3 5 5 3 3 4 5 5 5 4 4 5 4 5 4 4 4 5 6 6 1 6 6 6 6 2 3 2 4 2 4 5 5 4 5.0 4 4 5
73 74 22 1 3 1 1 1 1 1 2 2 5 1 1 1 2 1 2 1 2 5 5 4 5 3 3 3 3 4 5 4 5 4 4 4 4 5 5 5 5 3 3 3 5 5 3 5 4 5 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 3 3 5 5 5 5 5 4 5.0 5 3 5
74 75 22 1 3 2 1 1 1 1 4 2 3 1 1 1 1 2 1 1 3 4 4 4 4 4 4 4 3 3 4 4 4 4 4 4 4 4 4 4 4 2 2 2 2 4 2 2 4 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 2 2 3 2 2 4 3 2 4.0 2 2 2
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76 77 22 1 3 4 2 2 1 1 1 2 3 3 3 3 2 2 2 4 2 5 4 5 4 5 5 5 4 5 5 5 5 5 5 5 5 4 5 5 5 4 5 4 5 5 5 3 4 4 1 5 1 5 1 1 1 1 1 1 1 5 1 1 1 1 1 1 4 2 2 5 2 2 4 2 2 5.0 5 2 2
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78 79 22 1 3 2 3 1 1 3 2 2 3 1 2 1 1 1 1 5 3 5 4 3 3 4 4 2 4 4 5 4 4 4 4 3 2 2 4 4 4 4 4 5 4 2 4 5 5 5 5 1 1 1 5 1 3 5 1 1 5 2 2 1 4 6 1 2 4 2 2 4 2 2 4 4 4 4.0 4 3 3
79 80 24 1 3 2 1 1 1 5 4 1 1 2 2 2 1 1 1 6 1 3 3 2 3 4 4 2 3 3 3 3 3 4 4 3 3 3 4 4 4 4 4 4 4 3 3 3 3 4 3 3 1 5 5 1 2 3 1 4 3 2 6 3 3 6 6 2 4 3 4 3 3 3 4 3 3 5.0 4 4 3
80 81 22 1 3 2 2 1 2 1 2 1 4 3 3 3 1 1 2 4 4 4 5 4 5 5 4 3 5 5 4 3 5 5 4 5 3 4 5 4 4 4 5 4 3 4 5 4 5 3 1 1 4 2 1 3 2 1 3 2 1 5 3 4 2 3 1 6 5 3 4 5 4 5 3 5 4 4.0 5 3 5
81 82 25 1 3 2 3 1 2 1 3 1 3 1 2 3 2 1 1 7 1 3 3 3 4 3 4 3 4 3 4 3 2 5 3 2 3 5 4 3 2 1 2 3 3 3 3 4 4 3 5 2 1 1 5 1 2 1 1 3 1 1 4 1 5 1 1 1 2 2 2 4 2 4 4 4 4 4.0 3 3 2
82 83 21 1 3 4 2 1 1 4 1 3 3 2 2 2 1 2 1 5 2 5 4 2 3 5 3 2 1 5 3 4 1 3 2 3 4 4 3 2 4 3 5 3 3 3 5 5 5 5 5 3 3 4 2 3 2 3 1 2 1 5 6 3 5 4 5 6 5 4 3 5 3 4 1 3 1 1.0 1 3 5
83 84 22 1 3 2 1 1 1 1 3 3 1 2 2 3 2 2 1 2 1 3 4 4 4 5 5 5 4 5 4 5 4 4 4 4 4 4 5 5 4 3 2 1 3 1 1 2 1 4 1 1 1 2 3 1 1 1 1 1 1 3 3 3 3 3 3 3 3 3 1 5 1 1 5 4 1 5.0 4 1 2
84 85 23 1 3 2 1 1 2 1 3 2 2 1 1 1 2 2 1 2 1 4 4 5 5 5 5 4 4 5 4 3 5 3 4 4 3 5 4 4 2 3 4 3 4 3 5 4 4 3 5 5 1 1 5 1 1 1 1 1 1 5 3 1 3 3 3 2 5 4 3 5 4 2 3 3 2 2.0 5 2 1
85 86 21 1 3 2 1 1 1 1 2 2 5 2 3 3 2 2 1 2 3 5 3 4 4 4 4 3 5 5 5 5 5 5 5 4 4 4 4 4 5 4 5 4 4 4 4 5 4 4 1 1 1 1 1 1 1 1 1 1 1 3 1 1 1 1 1 1 4 4 4 4 4 4 4 4 4 4.0 4 4 4
86 87 23 1 2 4 3 2 1 4 2 4 3 2 1 1 1 1 3 1 4 4 4 4 4 3 3 3 4 4 4 4 4 5 5 5 5 5 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 1 1 1 1 1 1 1 3 2 2 5 4 3 5 4 4 4.0 3 4 4
87 88 22 1 3 2 3 1 2 2 2 2 2 1 1 2 2 2 1 5 1 4 4 3 4 4 3 3 4 5 3 3 4 3 2 3 3 3 4 4 2 1 1 1 1 1 1 1 4 4 4 3 2 4 4 4 5 4 3 3 4 2 2 6 6 6 5 6 3 2 2 4 1 2 4 4 3 4.0 4 2 1
88 89 29 1 4 2 3 1 1 1 1 3 3 2 1 1 1 1 3 1 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 4 4 5 5 3 3 5 2 3 4 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 2 2 5 2 3 5 4 4 4.0 2 2 2
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467 468 21 1 3 3 2 1 2 1 1 2 3 1 2 2 2 1 2 1 3 4 3 3 1 4 5 1 5 5 4 4 3 4 4 3 3 3 4 4 4 2 2 4 3 3 3 4 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 2 5 2 4 5 4 4 5.0 3 3 3
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469 470 23 1 3 2 2 1 1 1 2 2 1 1 1 2 1 2 2 3 2 5 4 3 3 4 5 3 4 5 5 4 4 4 3 3 3 3 4 3 4 3 3 3 3 3 3 2 3 4 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 3 4 4 3 3 3 5 3 4 3 3.0 3 3 3
470 471 25 1 3 2 1 1 1 3 2 3 1 1 2 2 2 1 1 2 1 5 5 4 3 3 3 3 5 4 4 4 4 4 3 3 3 3 3 3 5 5 5 5 5 4 4 4 5 5 2 1 1 1 4 4 3 3 3 3 3 1 1 3 3 3 3 3 2 2 2 4 3 4 5 5 4 5.0 5 4 3
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472 473 23 1 3 2 1 1 2 4 2 3 3 4 4 1 1 2 1 5 6 5 3 2 2 4 4 2 4 5 3 3 3 4 4 3 3 4 4 2 2 2 2 2 2 4 3 3 3 3 1 4 1 1 1 1 5 1 1 1 5 5 5 5 1 7 1 5 4 3 3 3 4 3 4 4 3 5.0 3 4 4
473 474 21 1 3 1 1 1 1 4 2 4 1 1 2 2 2 2 1 1 1 4 4 4 4 4 4 4 4 4 3 4 4 5 4 4 2 2 4 4 4 4 4 2 2 3 3 3 2 4 5 5 1 1 1 1 5 1 1 1 5 2 2 2 2 2 2 2 2 2 4 4 4 3 2 3 3 2.0 4 4 2
474 475 22 1 3 2 3 1 1 5 2 3 1 2 3 3 2 1 1 2 1 4 4 3 4 5 5 3 5 5 5 5 5 5 5 5 5 5 5 4 5 5 5 5 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3 3 3 3 3 1 1 2 5 3 5 5 4 4 5.0 5 5 3
475 476 22 1 3 1 2 1 1 1 2 2 3 1 4 4 2 1 1 1 3 2 5 5 4 5 4 3 1 5 4 3 5 4 4 4 1 1 4 4 5 5 5 4 4 3 4 3 2 3 5 5 1 1 1 1 5 2 1 2 3 7 7 1 1 1 5 7 4 3 2 5 4 4 5 5 4 4.0 5 5 4
476 477 20 1 3 2 1 1 1 5 2 3 2 1 1 1 2 2 1 1 2 5 5 5 5 4 4 4 4 5 5 4 3 4 4 4 4 4 4 4 4 1 1 4 4 4 4 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 2 2 4 2 2 5 4 2 4.0 5 3 2
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480 481 25 1 3 1 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1 5 4 3 2 3 5 2 4 4 5 3 3 3 2 2 2 2 3 5 3 4 3 3 3 3 3 2 3 3 1 1 5 1 5 1 3 1 5 1 1 3 3 1 3 3 3 3 3 3 3 5 3 3 5 3 3 4.0 3 3 3
481 482 22 1 3 2 1 1 1 5 2 2 3 2 2 1 1 1 1 1 3 5 5 5 5 5 5 4 3 5 5 5 5 5 5 5 4 4 5 5 5 2 2 2 5 4 2 2 4 4 3 5 1 1 2 1 4 1 2 3 1 1 1 1 1 1 1 1 5 4 4 5 4 5 4 5 4 5.0 4 4 3
482 483 22 1 3 6 1 1 1 3 2 2 1 1 2 2 2 2 4 2 1 4 3 2 3 3 4 4 4 4 4 4 5 5 5 5 5 4 4 4 5 5 1 1 5 4 1 4 5 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 4 3 4 4 4 4 4.0 5 4 4
483 484 24 1 3 2 2 1 2 4 2 2 2 2 2 3 1 2 2 3 2 5 3 2 2 3 5 2 3 4 4 3 3 4 3 2 2 2 3 5 3 3 2 2 3 2 3 2 3 3 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 3 3 3 4 3 2 5 3 3 3.0 3 3 3
484 485 22 1 3 2 3 1 1 1 3 2 1 1 1 1 2 2 1 4 1 5 5 3 3 5 5 3 5 4 5 4 4 4 4 3 3 3 4 4 4 3 3 3 4 3 3 3 4 4 5 5 1 1 3 1 5 1 1 1 1 2 1 5 4 5 5 3 2 1 1 4 1 2 4 3 3 5.0 4 3 2
485 486 28 1 3 2 1 1 2 1 2 2 2 1 1 1 1 2 1 3 2 5 4 3 3 3 5 3 4 4 5 4 4 4 3 3 3 3 3 5 5 3 3 2 4 3 4 3 4 4 1 2 1 3 1 2 3 3 2 1 1 4 4 4 4 4 4 4 4 4 4 5 4 3 5 3 3 5.0 3 5 4
486 487 23 1 3 2 2 1 1 3 2 2 3 1 1 1 1 2 1 5 1 1 4 3 4 5 4 3 3 4 5 3 5 4 5 5 3 4 4 3 3 5 4 3 5 3 3 2 3 3 5 3 4 1 5 1 2 1 1 3 1 3 5 6 3 4 6 6 4 2 2 5 1 2 5 4 4 4.0 3 4 2
487 488 22 1 1 2 1 1 2 1 2 2 1 1 1 1 1 2 1 1 1 5 4 3 3 3 5 3 4 4 5 4 4 4 3 3 3 3 4 5 5 4 3 2 4 3 3 2 3 4 5 1 4 3 2 1 1 2 1 1 1 2 1 1 3 3 1 1 3 3 3 5 4 3 5 4 3 5.0 3 4 3
488 489 24 1 3 4 1 1 1 3 4 3 3 1 1 1 1 1 1 4 3 5 5 5 5 5 5 3 2 5 3 4 5 4 5 3 4 4 5 5 5 1 1 5 5 1 1 3 4 5 3 3 5 3 1 3 3 1 3 1 1 2 3 3 1 3 2 1 5 5 4 3 5 5 5 5 4 5.0 5 4 3
489 490 23 1 3 2 1 1 2 5 3 1 1 1 1 1 1 2 1 4 1 5 3 2 2 2 2 2 4 5 4 4 4 4 3 3 3 4 4 4 4 4 3 4 4 4 4 4 4 4 2 3 2 3 2 4 2 3 2 2 2 5 3 5 5 5 5 5 3 3 4 5 4 3 5 3 4 5.0 4 3 3
490 491 22 1 3 1 1 2 1 3 4 1 1 1 1 1 1 2 1 6 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 1 1 1 1 1 1 1 1 1 1 7 7 7 7 7 7 7 4 2 3 5 3 4 4 4 4 4.0 3 4 4
491 492 22 1 3 2 1 1 1 1 4 1 4 1 1 1 1 2 1 1 3 4 4 3 3 3 4 2 4 5 4 3 3 5 3 2 2 3 4 5 3 3 3 4 3 2 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 5 2 2 5 4 3 4.0 4 2 2
492 493 23 1 3 2 1 1 1 1 2 4 3 1 1 1 1 1 1 1 3 4 4 3 3 3 3 3 4 4 4 4 5 5 5 4 4 4 4 4 4 5 5 4 4 4 4 5 4 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 4 3 4 3 3 4 4 3 4.0 3 3 3
493 494 22 1 3 2 2 1 1 1 3 3 2 3 2 2 1 1 2 1 2 5 5 5 4 5 5 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 4 5 5 5 5 5 5 5 5.0 4 5 4
494 495 21 1 3 2 1 1 1 3 4 1 1 1 1 1 1 1 1 5 6 5 5 4 4 5 5 4 5 5 5 5 5 5 5 4 4 4 5 5 5 4 4 4 5 5 5 4 4 5 4 3 1 1 1 1 1 1 1 1 1 6 6 6 6 6 6 6 4 4 2 5 5 5 5 4 2 5.0 2 2 2
495 496 23 1 3 2 1 1 1 1 1 2 3 1 1 1 1 1 1 1 1 5 4 3 3 4 5 3 1 5 5 5 5 5 5 5 5 4 4 5 5 2 1 1 5 1 1 1 4 3 3 3 1 2 1 3 5 4 1 1 5 1 1 1 1 1 1 1 3 2 3 4 4 2 4 3 3 5.0 5 3 1
496 497 24 1 3 6 2 2 1 3 1 2 2 2 1 2 1 1 1 2 2 5 4 3 3 3 5 3 4 4 4 4 4 4 3 3 3 3 3 3 3 3 4 2 4 3 4 3 4 3 1 2 3 2 3 2 3 2 3 2 1 3 3 3 3 3 3 3 5 4 4 4 3 4 3 4 3 4.0 3 3 4
497 498 25 1 3 3 1 1 1 3 1 2 2 2 1 2 1 2 1 4 2 5 4 3 3 3 4 3 3 5 4 4 4 4 3 4 3 4 3 4 3 4 3 2 4 3 3 4 3 3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3 4 4 3 3 3 4 3.0 3 3 3
498 499 24 1 3 3 2 1 2 2 2 2 3 2 2 2 1 2 2 2 3 5 4 3 3 3 5 3 4 4 4 4 3 3 3 3 3 3 4 5 4 4 3 3 4 3 3 3 2 4 5 4 5 5 3 5 3 5 5 5 5 3 3 3 3 3 3 3 4 3 3 4 3 3 5 4 3 3.0 4 3 3
499 500 25 1 3 1 1 1 2 1 3 1 1 2 2 2 2 2 1 1 1 4 4 2 3 4 5 2 3 4 5 3 5 4 2 1 2 2 3 3 4 2 2 2 2 4 1 3 5 3 5 3 1 1 5 1 3 1 1 2 1 5 5 5 5 2 1 5 3 4 2 5 1 2 5 3 2 4.0 5 1 3
500 501 21 1 3 2 2 2 1 1 2 3 4 2 3 4 2 2 1 1 4 5 5 5 5 5 5 5 3 5 5 4 4 4 3 3 3 3 3 3 5 2 1 5 4 4 1 1 4 3 5 5 5 5 5 5 5 5 5 5 1 1 1 1 1 1 1 1 5 5 3 5 3 3 5 5 3 5.0 5 5 3
501 502 23 1 3 2 2 1 1 1 2 2 4 1 1 1 1 1 1 1 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 4 3 4 4 4 2 1 1 1 1 1 1 1 1 1 1 6 3 3 4 6 3 3 1 4 1 4 1 1 4 4 4 4.0 4 1 1
502 503 22 1 3 2 1 1 1 5 2 3 3 1 2 1 2 1 1 4 3 4 4 4 4 5 5 4 4 4 3 4 5 4 5 4 4 4 4 3 4 2 2 4 4 3 3 3 3 4 5 1 1 1 5 1 1 1 1 1 1 2 3 1 1 3 1 1 4 3 3 5 3 3 5 4 4 5.0 5 2 1
503 504 26 1 3 2 1 1 2 1 4 1 2 1 1 1 2 1 1 2 2 3 5 4 2 1 4 5 3 5 4 4 5 2 4 3 5 2 1 4 5 4 2 4 1 5 5 5 4 3 3 1 2 2 1 2 5 2 1 2 3 2 3 3 1 4 2 6 3 5 3 4 3 3 5 2 4 1.0 5 4 2
504 505 21 1 3 6 4 1 1 1 2 2 4 2 3 4 2 1 1 2 4 5 5 5 3 5 4 5 1 5 5 5 5 5 5 5 5 4 5 5 5 5 4 5 5 5 5 5 3 5 5 5 1 1 1 1 1 1 1 1 5 3 3 3 3 3 3 3 5 5 3 5 3 4 5 5 4 5.0 5 4 3
505 506 23 1 3 2 2 1 1 4 2 2 2 2 3 3 1 1 2 6 3 4 5 4 1 3 4 5 3 5 4 3 3 3 4 3 4 1 3 4 3 5 3 4 5 4 5 5 5 4 2 3 5 3 2 3 2 4 2 3 2 2 3 2 3 4 5 4 4 5 3 1 3 4 2 4 3 5.0 3 4 2
506 507 23 1 3 2 2 1 1 1 1 2 1 1 1 1 1 1 1 4 3 5 5 5 4 5 5 5 3 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 5 5 4 3 3 1 1 1 5 4 1 1 1 1 5 1 5 5 5 5 5 5 5 4 3 4 2 3 3 2 3 3.0 2 1 2
507 508 21 0 3 4 4 2 1 1 3 3 3 2 2 3 2 1 1 4 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 4 5 5 3 5 5 5 5 5 5 5 1 5 1 1 5 1 1 1 1 1 5 5 5 5 5 5 5 5 5 5 3 5 3 4 5 5 4 5.0 5 4 3
508 509 27 1 3 2 2 1 1 1 4 2 3 1 1 1 1 1 1 2 1 5 4 3 3 3 4 3 5 3 5 4 4 4 3 3 3 3 4 4 4 4 3 3 3 3 3 4 4 3 3 1 1 1 1 1 1 1 1 1 1 3 4 3 2 3 3 3 3 3 4 5 3 3 5 4 4 5.0 3 4 3
509 510 22 1 3 2 4 1 1 4 2 4 4 1 4 3 2 2 1 3 4 4 4 3 2 3 3 1 3 4 4 4 4 4 2 1 1 1 3 3 4 4 4 4 4 4 3 4 4 4 5 5 5 5 5 5 5 5 5 5 5 4 1 4 5 5 1 3 2 2 2 4 1 3 4 5 3 4.0 4 4 2
510 511 22 1 3 3 2 1 2 1 2 2 3 2 2 4 1 1 1 1 4 4 4 4 5 2 1 1 1 5 5 5 5 5 1 4 4 4 4 4 4 4 4 4 4 4 4 4 1 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 5 1 1 5 4 5 5.0 1 1 1
511 512 21 1 3 4 1 1 1 1 3 1 3 1 2 3 2 1 1 1 3 5 4 5 3 4 3 3 3 5 5 3 4 3 3 3 3 5 3 3 2 2 2 2 3 3 2 2 4 3 5 1 1 1 1 5 5 1 1 1 1 4 1 4 5 1 1 4 4 2 2 4 2 1 4 4 3 5.0 5 2 3
512 513 23 1 3 1 1 1 1 1 3 2 3 1 1 1 1 1 1 1 2 4 4 4 4 4 4 4 2 4 4 4 4 4 4 4 4 4 4 4 3 3 4 3 4 4 3 3 3 3 5 1 1 1 5 5 2 2 1 1 1 1 3 1 3 1 3 1 3 2 2 5 2 2 5 5 5 5.0 4 4 3
513 514 21 0 3 4 2 2 1 1 3 3 4 2 2 2 1 1 2 1 3 5 4 3 3 4 4 3 5 4 5 4 5 5 3 3 2 2 3 3 5 4 4 4 3 3 3 3 4 3 2 1 1 4 1 2 2 3 1 1 1 5 4 1 1 3 1 1 3 2 2 4 3 3 5 5 3 4.0 4 5 3
514 515 22 1 3 6 1 1 1 1 2 4 3 1 1 1 1 2 1 3 3 5 5 2 2 5 3 1 5 5 5 5 5 5 5 1 1 1 1 1 5 5 5 5 1 1 5 5 3 4 5 2 1 2 3 2 5 1 1 1 5 6 5 5 4 6 5 5 4 1 1 4 4 3 5 5 3 5.0 2 4 1
515 516 22 1 4 6 1 1 2 4 4 1 3 1 1 1 1 1 1 1 3 5 5 4 3 4 5 3 4 4 3 3 3 3 2 3 3 3 3 4 4 3 3 3 3 3 3 5 4 4 5 3 1 2 5 1 1 1 1 4 1 1 1 1 5 5 1 1 2 2 1 3 1 1 4 3 3 4.0 2 1 1
516 517 25 0 4 4 4 2 1 1 2 4 6 1 1 1 1 1 4 3 6 5 4 4 3 5 5 1 1 5 5 5 5 5 5 5 3 4 3 3 5 5 5 5 3 5 5 5 5 5 1 1 1 1 1 1 1 1 1 1 1 4 4 4 4 4 4 4 2 2 2 5 2 2 5 5 5 5.0 3 3 3
517 518 18 0 2 4 4 1 1 4 1 3 3 1 1 1 1 2 1 3 3 4 4 4 4 4 4 4 4 5 5 5 4 5 5 4 4 4 4 4 5 5 5 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 3 3 3 5 4 5 5 5 4 5.0 5 5 5
518 519 21 1 3 4 3 1 1 1 1 2 1 1 1 1 1 2 1 1 1 4 4 4 3 3 4 3 4 4 4 4 4 4 4 3 3 3 3 4 3 2 2 2 5 5 2 3 4 4 5 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 5 4 5 5 5 3 5.0 5 5 5
519 520 22 1 3 4 2 2 1 1 1 2 2 1 1 2 1 2 1 3 2 3 4 4 4 4 4 3 4 5 4 4 4 4 4 4 4 4 4 4 2 2 2 2 5 5 2 3 4 4 5 5 3 1 3 1 3 1 1 1 1 3 3 3 3 3 3 3 4 3 3 5 4 4 5 5 3 5.0 5 5 5
520 521 18 0 2 4 2 2 1 2 2 1 3 1 1 1 1 2 1 5 3 4 3 3 3 4 4 3 4 4 3 4 4 4 3 3 3 3 4 4 4 2 2 2 2 4 2 3 3 4 5 5 1 4 4 4 4 4 1 4 4 6 6 6 6 6 6 6 4 3 3 5 2 5 5 3 3 5.0 5 5 5
521 522 21 1 3 2 1 2 1 1 1 2 3 1 1 1 1 2 1 4 3 4 4 3 3 4 4 3 4 4 4 4 4 4 3 3 3 3 4 4 3 2 2 2 4 4 2 3 4 4 5 5 1 1 1 1 1 1 1 1 1 5 5 5 5 5 5 5 3 3 3 5 4 5 5 3 4 5.0 5 5 5
522 523 20 1 2 4 2 1 1 3 2 2 3 1 1 2 1 2 1 1 3 4 4 3 3 4 4 3 4 4 4 4 4 4 4 3 3 3 4 4 3 2 2 2 5 5 2 3 4 5 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 3 3 3 5 4 4 5 4 4 5.0 5 5 5
523 524 23 1 3 4 3 2 1 3 1 2 3 1 1 1 1 2 1 1 3 5 5 5 4 4 5 3 4 5 4 4 4 4 3 5 3 3 3 5 4 2 2 2 5 5 2 3 4 4 3 3 3 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 3 3 2 5 4 4 5 3 3 5.0 5 5 5
524 525 23 0 3 4 2 1 1 1 1 2 2 1 1 1 1 2 1 2 2 4 4 3 3 3 3 3 4 4 4 4 4 4 4 3 3 3 3 3 4 2 2 2 2 4 1 3 4 4 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 3 2 2 1 5 3 3 5 3 3 5.0 5 5 5
525 526 22 0 3 4 2 1 2 5 4 2 1 1 1 2 1 1 1 2 1 3 4 3 3 4 4 3 3 4 4 4 4 4 4 4 4 3 4 4 3 2 2 2 3 3 3 3 3 3 5 1 1 1 1 1 1 2 2 2 2 3 3 3 1 3 3 4 3 3 3 5 3 3 5 3 3 5.0 5 5 5
526 527 22 0 3 4 4 1 1 5 1 4 3 1 1 2 1 2 1 1 3 5 5 5 4 5 4 4 5 5 5 5 5 5 4 4 4 4 5 5 4 4 4 4 5 5 4 4 4 5 5 5 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 3 3 3 5 3 4 5 4 5 5.0 5 5 5
In [51]:
#Creating a new dataframe to reverse code some features

data2['Gender']=data2['Gender'].map(Gender_Code)
data2['Education']=data2['Education'].map(Edu_Code)
data2['Father Occupation']=data2['Father Occupation'].map(Parent_Occu_Code)
data2['Household Income']=data2['Household Income'].map(Household_Income_Code)
data2['Native (Rural/Urban)']=data2['Native (Rural/Urban)'].map(Nativity_Code)
data2['Handset Brand']=data2['Handset Brand'].map(Handset_Brand_Code)
data2['Handset Selection Reason']=data2['Handset Selection Reason'].map(Handset_Selection_Code)
data2['Handset Cost']=data2['Handset Cost'].map(Handset_Cost_Code)
data2['Handset Age']=data2['Handset Age'].map(Handset_Age_Code)
data2['Calls Receiving per day']=data2['Calls Receiving per day'].map(Calls_Rec_Code)
data2['SMS Receiving per day']=data2['SMS Receiving per day'].map(SMS_Rec_Code)
data2['SMS Sending per day']=data2['SMS Sending per day'].map(SMS_Send_Code)
data2['Monthly Mobile Expenditure']=data2['Monthly Mobile Expenditure'].map(Mobile_Expend_Code)
data2['Current Service Provider']=data2['Current Service Provider'].map(Service_Provider_Code)
data2['Current Service Provider Age']=data2['Current Service Provider Age'].map(Service_Provider_Age_Code)
In [52]:
sns.countplot(x='Education',data=data2)
plt.xticks( rotation=45, ha='right')  # Adjust rotation and ha as needed
Out[52]:
(array([0, 1, 2, 3]),
 [Text(0, 0, '2-U.G.'),
  Text(1, 0, '3-P.G.'),
  Text(2, 0, '4-Research scholars'),
  Text(3, 0, '1-Intermediate/equivalent')])
In [53]:
sns.countplot(x='Education'
              ,data=data2
              ,hue='Gender')
plt.xticks( rotation=45, ha='right')  # Adjust rotation and ha as needed
Out[53]:
(array([0, 1, 2, 3]),
 [Text(0, 0, '2-U.G.'),
  Text(1, 0, '3-P.G.'),
  Text(2, 0, '4-Research scholars'),
  Text(3, 0, '1-Intermediate/equivalent')])
In [54]:
sns.countplot(x='Gender'
              ,data=data2)
Out[54]:
<Axes: xlabel='Gender', ylabel='count'>
In [55]:
data2.iloc[:,4].value_counts()
Out[55]:
2-Farmer           204
4-Employee         162
5-Business          63
3-Self-Employed     38
1-Coolie            37
6-Others            23
Name: Father Occupation, dtype: int64
In [56]:
data2.iloc[:,4].map(Parent_Occu_Code).value_counts()
Out[56]:
Series([], Name: Father Occupation, dtype: int64)
In [57]:
sns.countplot(x='Father Occupation'
              ,data=data2
              )

plt.xticks( rotation=45, ha='right')  # Adjust rotation and ha as needed
plt.show()
In [58]:
sns.countplot(x='Household Income'
              ,data=data2)
plt.xticks( rotation=45, ha='right')  # Adjust rotation and ha as needed
Out[58]:
(array([0, 1, 2, 3]),
 [Text(0, 0, '2-Rs.5000-10000'),
  Text(1, 0, '1-<Rs.5000'),
  Text(2, 0, '3-Rs.10000-15000'),
  Text(3, 0, '4->15000')])
In [59]:
sns.countplot(x='Handset Brand'
              ,data=data2)
Out[59]:
<Axes: xlabel='Handset Brand', ylabel='count'>
In [60]:
sns.countplot(x='Handset Cost'
              ,data=data2)
Out[60]:
<Axes: xlabel='Handset Cost', ylabel='count'>
In [61]:
Handset_cost = pd.DataFrame(data2.groupby('Handset Cost')['Handset Cost'].count())
Handset_cost['Handset cost%']=Handset_cost['Handset Cost']/Handset_cost['Handset Cost'].sum()*100

Handset_cost
Out[61]:
Handset Cost Handset cost%
Handset Cost
1-<Rs.1500 89 16.888046
2-Rs.1500-2500 224 42.504744
3-Rs.2500-5000 144 27.324478
4->Rs.5000 70 13.282732
In [62]:
plt.figure(figsize=(5,5))
plt.pie(Handset_cost['Handset Cost'], autopct="%1.1f%%")
plt.show()
In [63]:
Handset_cost2=data2.dropna(subset=["Handset Cost"])
Handset_cost2=data2.groupby("Handset Cost")
Handset_cost21 = Handset_cost2.size().sort_values(ascending = False)
Handset_cost21 = Handset_cost21.to_frame('Count').reset_index()
Handset_cost21['Handset Cost %']=Handset_cost21['Count']/Handset_cost21['Count'].sum()*100

Handset_cost21
Handset_cost21.head(10)
Out[63]:
Handset Cost Count Handset Cost %
0 2-Rs.1500-2500 224 42.504744
1 3-Rs.2500-5000 144 27.324478
2 1-<Rs.1500 89 16.888046
3 4->Rs.5000 70 13.282732
In [64]:
plt.figure(figsize=(5,5))
plt.pie(Handset_cost21['Count'],labels=Handset_cost21["Handset Cost"], autopct="%1.1f%%")
plt.show()
In [65]:
plt.figure(figsize=(5,5))
colors = ['green', 'blue', 'orange', 'red', 'yellow', 'indigo']

#plt.bar(x=Handset_cost21["Handset Cost"].map(Handset_Cost_Code),height=Handset_cost21['Handset Cost %'],color=colors)
plt.bar(x=Handset_cost21["Handset Cost"],height=Handset_cost21['Handset Cost %'],color=colors
       )

# Set x-axis tick positions and labels
#plt.xticks(Handset_cost21["Handset Cost"], labels=Handset_cost21["Handset Cost"].map(Handset_Cost_Code),rotation=35)

plt.xticks(rotation=35)

#plt.legend()
# plt.xlabel('Handset Cost')
# plt.ylabel('Handset Cost Percentage')
# plt.title('Handset Cost Distribution')

plt.show()
In [66]:
# Assuming 'data2' is your DataFrame
columns_of_interest = [ 'Gender', 'Education', 'Father Occupation',
       'Household Income','Native (Rural/Urban)', 'Handset Brand',
       'Handset Selection Reason', 'Handset Cost', 'Handset Age',
       'Calls Receiving per day', 'SMS Receiving per day',
       'SMS Sending per day',  'Monthly Mobile Expenditure',
       'Current Service Provider', 'Current Service Provider Age',]

for column in columns_of_interest:
    # Drop NaN values and group by the specified column
    data2[column] = data2[column].astype('category')
    grouped_data = data2.dropna(subset=[column]).groupby(column)
    
    # Create a DataFrame with counts and percentages
    data_grouped = grouped_data.size().to_frame('Count').reset_index()
    data_grouped['Percentage'] = data_grouped['Count'] / data_grouped['Count'].sum() * 100
    
    # Sort the DataFrame by the column of interest
    data_grouped = data_grouped.sort_values(by=column, ascending=True)

    # Plot the bar chart
    plt.figure(figsize=(5, 5))
    colors = ['green', 'blue', 'orange', 'red', 'yellow', 'indigo','cyan']
    
    plt.bar(x=data_grouped[column], height=data_grouped['Percentage'], color=colors, alpha=0.7)
    plt.xticks(range(len(data_grouped)), rotation=35)
    plt.xlabel(column)
    plt.ylabel('Percentage')
    plt.title(f'Distribution of {column}')
    plt.show()
In [67]:
# Assuming 'data2' is your DataFrame
columns_of_interest = ['Gender', 'Education', 'Father Occupation',
                        'Household Income', 'Native (Rural/Urban)','Handset Brand',
                        'Handset Selection Reason', 'Handset Cost', 'Handset Age',
                        'Calls Receiving per day', 'SMS Receiving per day',
                        'SMS Sending per day', 'Monthly Mobile Expenditure',
                        'Current Service Provider', 'Current Service Provider Age',]

for column in columns_of_interest:
    # Drop NaN values and group by the specified column
    data2[column] = data2[column].astype('category')
    grouped_data = data2.dropna(subset=[column]).groupby(column)

    # Create a DataFrame with counts and percentages
    data_grouped = grouped_data.size().to_frame('Count').reset_index()
    data_grouped['Percentage'] = data_grouped['Count'] / data_grouped['Count'].sum() * 100

    # Sort the DataFrame by the column of interest
    data_grouped = data_grouped.sort_values(by=column, ascending=True)

    # Calculate the number of categories
    num_categories = len(data_grouped)

    # Adjust the figure size based on the number of categories
    fig_size = (min(8, num_categories * 1.1), min(6, num_categories*1.1))  # Minimum figure size of 8 inches

    # Plot the bar chart
    plt.figure(figsize=fig_size)
    colors = ['green', 'blue', 'orange', 'red', 'yellow', 'indigo', 'cyan']

    bars = plt.bar(x=data_grouped[column], height=data_grouped['Percentage'], color=colors, alpha=0.7)

    # Add value labels on each bar
    for bar in bars:
        yval = bar.get_height()
        plt.text(bar.get_x() + bar.get_width() / 2, yval, f'{round(yval, 1)}%', ha='center', va='bottom')

    plt.xticks(range(len(data_grouped)), rotation=35)
    plt.xlabel(column)
    plt.ylabel('Percentage')
    plt.title(f'Distribution of {column}')
    plt.show()

Analysis of Association¶

2.1 Introduction¶

In this Chapter we make an attempt to describe some of the features of the sample under study. Particularly, we are interested in knowing whether there exists any association be- tween a few selected pairs of classification variables; if an association exists, then to know the strength and direction of the association. The classification variables we come across in our project work are of categorical in nature. Categorical variables are usually classified as being of two basic types: nominal and ordinal. Nominal variables involve categories that have no particular order. Sex, make of hand set, occupation, birth place etc are examples of nominal variables. Categories associated with an ordinal variable have some inherent ordering. Some examples of ordinal variables are income groups, monthly expenditure towards mobile, years of education etc. Statisticians have developed a number of techniques to analyze and explain categorical data. One such technique that examines the relationship between two categorical variables is contingency table analysis. In Section 2, we briefly review contingency tables, statistical measures that detect the association between the classification variables of a con- tingency table, and measures that quantify the association are described. These are then used in the analysis of the contingency tables.

The cross tables analysis is used to answer questions such as:

  • Are the levels of cost of the hand-set independent of the levels of parents’ income ?
  • Are the levels of monthly expenditure for the mobile independent of the level of the parents income?
  • Is the monthly expenditure the same among the male and female students ?
  • Is the monthly expenditure among rural and urban students the same?

2.2 Contingency Table Analysis¶

It is a technique that describes two or more variables simultaneously. It results in tables that reflects the joint distributions of two or more variables that have a finite number of categories or distinct values. It is a technique of merging the frequency distributions of two or more variables into a single table. It helps to understand whether one classification variable such as Income relates to another classification variable such as the Handset Cost owned by an individual. In general, the margins of a cross table show the same information as the frequency tables of the individual variables. These tables are also known as contingency tables. Cross tables are widely used because a series of cross tabulations may give good insight of a complex phenomenon than a single multivariate analysis.

In the analysis of cross tables, we are primarily interested in

  • testing whether two samples are homogeneous
  • testing the significance of association between the classification variables and
  • measuring the strength of the association between the classification variables.

2.3 Chi-square Test¶

To test the homogeneity as well as the statistical significance of the observed association in a cross table, we usually use the Chi-square statistic.

2.3 Chi-square Test¶

To test the homogeneity as well as the statistical significance of the observed association in a cross table, we usually use the Chi-square statistic.

2.3.1 Test for Independence¶

The Chi-square statistic is used to determine whether a systematic association exists between two classification variables. We test the null hypothesis,

H0 = There is no association between two classification variables.

Ha = The two variables are associated.

The test is proceeded by computing the expected cell frequencies if no association were present between the variables, given the row and column totals. In an I × J cross-table, the expected frequency of any cell is obtained by the product of the corresponding row and column totals divided by the grand total. Let nI , nJ , and n denoted the number of observations in a row , in a column and in the table respectively. Then the expected frequencies corresponding to a cell is computed as follows:

image-2.png

After computing the expected cell frequencies, they are compared to the observed cell fre- quencies(O). Their discrepancy is quantified by computing

image.png

The calculated value of chi square is then compared with the tabulated value for a given d.f (I-1)(J-1) at a specified level of significance alpha ,where I denotes the no.of rows and J denotes the no. of columns.

2.3.2 Test for Homogeneity¶

To apply this test procedure we assume that separate random samples are obtained from each of two or more populations to determine whether the responses related to a single categorical variable are consistent across populations. The Chi-square test is conducted in exactly the same way as in the case of test for independence. The only difference in the analysis is in the statement of the hypotheses and the conclusions made.

The hypotheses are stated as follows:

H0 = The distribution of categorical variable is the same across the populations .

Ha = The distribution of categorical variable differs across the populations .

2.4 Measures of Association¶

If the association between the classification variables is found to be significant, then we want to measure it. These measures are grouped into two categories nominal and ordinal basing on the classification variables involved. If the classification variables are nominal, then the association between them is measured by using statistics such as

  1. Phi Correlation Coefficient,
  2. Contingency Coefficient,
  3. Cramer’s V,
  4. Lambda Coefficient.

On the other hand, if the classification variables are ordinal, then we use the measures such as

  1. Gamma,
  2. Kendall’s Tau-b,
  3. Kendall’s Tau-c,
  4. Somers’ d.

2.5 INCOME × COST¶

It might be argued that INCOME forms a series of strata, so that it should be considered as a factor. A similar statement can be made with respect to the COST as well. Since INCOME and COST of the mobile were queried simultaneously, we will treat both of them as random. The usual assumptions lead to a multinomial distribution with I×J levels. That is, let Yij denote the count in the (i, j) cell for a sample of size n. Then, the multinomial parameters are pij , which are the probabilities of a randomly selected individual being at level i of the first response and level j of the second response. Under this model there is only one constraint, which can be written in two ways: image.png

The corresponding null hypothesis is that the variables are independent. This means that the probabilities of the joint events in the cells can be written in terms of the marginal probabilities. That is, image-2.png

This hypothesis is to tested against the alternative hypothesis image-3.png

The income groups and the costs of the mobile phones are coded as follows:

Income Groups Income Group Code
< 5,000 1
5,000 - 10,000 2
10,000 - 15,000 3
> 15,000 4
Handset Cost Handset Cost Code
< 1,500 1
1,500 - 2,500 2
2,500 - 5,000 3
> 5,000 4
In [68]:
data2.groupby(['Household Income']).size()
Out[68]:
Household Income
1-<Rs.5000          171
2-Rs.5000-10000     177
3-Rs.10000-15000    111
4->15000             68
dtype: int64
In [69]:
data2.groupby(['Household Income', 'Handset Cost']).size()
Out[69]:
Household Income  Handset Cost  
1-<Rs.5000        1-<Rs.1500        44
                  2-Rs.1500-2500    83
                  3-Rs.2500-5000    36
                  4->Rs.5000         8
2-Rs.5000-10000   1-<Rs.1500        26
                  2-Rs.1500-2500    88
                  3-Rs.2500-5000    47
                  4->Rs.5000        16
3-Rs.10000-15000  1-<Rs.1500        15
                  2-Rs.1500-2500    38
                  3-Rs.2500-5000    38
                  4->Rs.5000        20
4->15000          1-<Rs.1500         4
                  2-Rs.1500-2500    15
                  3-Rs.2500-5000    23
                  4->Rs.5000        26
dtype: int64
In [70]:
# Assuming 'data2' is your DataFrame
columns_of_interest = ['Household Income', 'Handset Cost']

# Filter the DataFrame for the selected columns
selected_data = data2[columns_of_interest].dropna()

# Calculate the percentages within each category
percentages = selected_data.groupby(['Household Income', 'Handset Cost']).size() / selected_data.groupby(['Household Income']).size() * 100
percentages = percentages.reset_index(name='Percentage')

# Create a grouped bar chart using Seaborn
plt.figure(figsize=(8,6))
ax = sns.barplot(data=percentages, x='Household Income', y='Percentage', hue='Handset Cost', palette='rainbow')

# Add data labels for each bar
for p in ax.patches:
    ax.annotate(f'{p.get_height():.1f}%', (p.get_x() + p.get_width() / 2., p.get_height()),
                ha='center', va='center', xytext=(0, 10), textcoords='offset points')

# Move the legend outside the plot
ax.legend(loc='upper left', bbox_to_anchor=(1, 1))
    
plt.xlabel('Household Income')
plt.ylabel('Percentage')
plt.title('Distribution of Household Income and Handset Cost (Percentage)')
plt.show()

From the above graph we can observe that

  • As the Income level increases, the proportion of students using costly handsets increases.
  • As the Income level increases, the proportion of students using low-cost handsets decreases.
  • In Income groups < 5,000 as well as 5,000−10,000, almost 50% of students are using handsets with a cost in the range of 1,500−2,500.

From the above inferences, it seems that there is some association between Income level and Hand-set cost. We will verify this by constructing a Income × Cost cross-table. Our data set has resulted in the following cross-table for Income versus Cost of the mobile.

In [71]:
#The cross table for Income × Cost has resulted in the following:


# Assuming 'data2' is your DataFrame
columns_of_interest = ['Household Income', 'Handset Cost']

# Filter the DataFrame for the selected columns
selected_data = data2[columns_of_interest].dropna()

# Create a crosstabulation
cross_tab = pd.crosstab(selected_data['Handset Cost'],selected_data['Household Income']
                        , margins=True, margins_name='Total')

# Display the crosstab
print(cross_tab)
cross_tab
Household Income  1-<Rs.5000  2-Rs.5000-10000  3-Rs.10000-15000  4->15000  \
Handset Cost                                                                
1-<Rs.1500                44               26                15         4   
2-Rs.1500-2500            83               88                38        15   
3-Rs.2500-5000            36               47                38        23   
4->Rs.5000                 8               16                20        26   
Total                    171              177               111        68   

Household Income  Total  
Handset Cost             
1-<Rs.1500           89  
2-Rs.1500-2500      224  
3-Rs.2500-5000      144  
4->Rs.5000           70  
Total               527  
Out[71]:
Household Income 1-<Rs.5000 2-Rs.5000-10000 3-Rs.10000-15000 4->15000 Total
Handset Cost
1-<Rs.1500 44 26 15 4 89
2-Rs.1500-2500 83 88 38 15 224
3-Rs.2500-5000 36 47 38 23 144
4->Rs.5000 8 16 20 26 70
Total 171 177 111 68 527
In [72]:
# Assuming 'data2' is your DataFrame
columns_of_interest = ['Household Income', 'Handset Cost']

# Filter the DataFrame for the selected columns
selected_data = data2[columns_of_interest].dropna()

# Create a crosstabulation with observed counts
observed_cross_tab = pd.crosstab(selected_data['Handset Cost'], selected_data['Household Income'], margins=True, margins_name='Total')

# Calculate expected counts using chi-squared test
_, _, _, expected = chi2_contingency(observed_cross_tab)

# Create a DataFrame for expected counts
expected_cross_tab = pd.DataFrame(expected, columns=observed_cross_tab.columns, index=observed_cross_tab.index)

# Display both observed and expected crosstabs
# print("Observed Counts:")
# print(observed_cross_tab)

# print("\nExpected Counts:")
# print(expected_cross_tab)

# Combine observed and expected counts into a single DataFrame
combined_cross_tab = pd.concat([observed_cross_tab, expected_cross_tab], keys=['Observed', 'Expected'], axis=0)

# Display the combined crosstab
#print(combined_cross_tab)

combined_cross_tab
Out[72]:
Household Income 1-<Rs.5000 2-Rs.5000-10000 3-Rs.10000-15000 4->15000 Total
Handset Cost
Observed 1-<Rs.1500 44.000000 26.000000 15.000000 4.000000 89.0
2-Rs.1500-2500 83.000000 88.000000 38.000000 15.000000 224.0
3-Rs.2500-5000 36.000000 47.000000 38.000000 23.000000 144.0
4->Rs.5000 8.000000 16.000000 20.000000 26.000000 70.0
Total 171.000000 177.000000 111.000000 68.000000 527.0
Expected 1-<Rs.1500 28.878558 29.891841 18.745731 11.483871 89.0
2-Rs.1500-2500 72.683112 75.233397 47.180266 28.903226 224.0
3-Rs.2500-5000 46.724858 48.364326 30.330171 18.580645 144.0
4->Rs.5000 22.713472 23.510436 14.743833 9.032258 70.0
Total 171.000000 177.000000 111.000000 68.000000 527.0
In [73]:
# Assuming 'data2' is your DataFrame
columns_of_interest = ['Household Income', 'Handset Cost']

# Filter the DataFrame for the selected columns
selected_data = data2[columns_of_interest].dropna()

# Create a crosstabulation with observed counts
observed_cross_tab = pd.crosstab(selected_data['Handset Cost'], selected_data['Household Income'], margins=True, margins_name='Total')

# Calculate expected counts using chi-squared test
_, _, _, expected = chi2_contingency(observed_cross_tab)

# Create DataFrames for observed and expected counts
observed_df = pd.DataFrame(observed_cross_tab.values, columns=observed_cross_tab.columns, index=observed_cross_tab.index)
expected_df = pd.DataFrame(expected, columns=observed_cross_tab.columns, index=observed_cross_tab.index)

# Calculate percentages within each income group
observed_percentages = observed_df.div(observed_df.loc['Total']) * 100
#expected_percentages = expected_df.div(expected_df.loc['Total']) * 100

# Combine observed and expected counts and percentages into a single DataFrame
combined_cross_tab = pd.concat([observed_df, expected_df, observed_percentages], axis=0,
                               keys=['Observed', 'Expected',  'Observed (%)'])

# Display the combined crosstab with percentages
#print(combined_cross_tab)
combined_cross_tab
Out[73]:
Household Income 1-<Rs.5000 2-Rs.5000-10000 3-Rs.10000-15000 4->15000 Total
Handset Cost
Observed 1-<Rs.1500 44.000000 26.000000 15.000000 4.000000 89.000000
2-Rs.1500-2500 83.000000 88.000000 38.000000 15.000000 224.000000
3-Rs.2500-5000 36.000000 47.000000 38.000000 23.000000 144.000000
4->Rs.5000 8.000000 16.000000 20.000000 26.000000 70.000000
Total 171.000000 177.000000 111.000000 68.000000 527.000000
Expected 1-<Rs.1500 28.878558 29.891841 18.745731 11.483871 89.000000
2-Rs.1500-2500 72.683112 75.233397 47.180266 28.903226 224.000000
3-Rs.2500-5000 46.724858 48.364326 30.330171 18.580645 144.000000
4->Rs.5000 22.713472 23.510436 14.743833 9.032258 70.000000
Total 171.000000 177.000000 111.000000 68.000000 527.000000
Observed (%) 1-<Rs.1500 25.730994 14.689266 13.513514 5.882353 16.888046
2-Rs.1500-2500 48.538012 49.717514 34.234234 22.058824 42.504744
3-Rs.2500-5000 21.052632 26.553672 34.234234 33.823529 27.324478
4->Rs.5000 4.678363 9.039548 18.018018 38.235294 13.282732
Total 100.000000 100.000000 100.000000 100.000000 100.000000
Clearly, there are discrepancies between the actual frequencies and the expected frequencies. Are these differences statistically significant? To know this, Pearson’s Chi-square test is conducted, and the results are presented in the following table:¶
In [74]:
# Assuming 'data2' is your DataFrame
columns_of_interest = ['Household Income', 'Handset Cost']

# Filter the DataFrame for the selected columns
selected_data = data2[columns_of_interest].dropna()

# Create a crosstabulation with observed counts
observed_cross_tab = pd.crosstab(selected_data['Handset Cost'], selected_data['Household Income'])

# Perform chi-squared test
chi2_stat, p_value, dof, expected = chi2_contingency(observed_cross_tab)

# Calculate minimum expected count
min_expected_count = np.min(expected)

# Count cells with expected count less than 5
cells_with_low_expected_count = np.sum(expected < 5)
cells_with_low_expected_count_pct=cells_with_low_expected_count/(observed_cross_tab.size)

# Display chi-square test results in a table
table = [
    ["Pearson Chi-Square", chi2_stat],
    ["Likelihood Ratio", chi2_contingency(observed_cross_tab, lambda_='log-likelihood')[0]],
    ["Linear-by-Linear Association", chi2_contingency(observed_cross_tab, correction=True)[0]],
    ["N of Valid Cases", selected_data.shape[0]],
    ["df", dof],
    ["Asymp. Sig (2-sided) - P-value", p_value],
    ["Minimum Expected Count", min_expected_count],
    [f"{cells_with_low_expected_count} cells ({cells_with_low_expected_count_pct}%) have expected count less than 5."]
]

print("\nChi-Square Test Results:")
print(tabulate(table, headers=[ "Value"]))
Chi-Square Test Results:
                                                         Value
-----------------------------------------------  -------------
Pearson Chi-Square                                77.3253
Likelihood Ratio                                  71.8236
Linear-by-Linear Association                      77.3253
N of Valid Cases                                 527
df                                                 9
Asymp. Sig (2-sided) - P-value                     5.48211e-13
Minimum Expected Count                             9.03226
0 cells (0.0%) have expected count less than 5.

From the table we note that the p − value < 0.05 and hence the assumption of independence between the classification variables is rejected. Thus, there is some dependence between the factor levels of Income and Cost. Our next problem is to find out the strength of the association between the factor levels of Income and Cost of the mobile.

To measure the strength of association between categorical variables, SPSS offers several measures of association. These measures are grouped into two categories namely nominal and ordinal. Note that both the classification variables in the above crosstable are ordinal in nature. Thus, it is appropriate to use ordinal measure of association to assess the strength. Somers’ d is one such measure. Somers’ d is an ordinal directional measure that indicates the significance, strength and direction of the relationship between the row and column variables of a cross tabulation. A low significance value (typically less than 0.05) indicates that there is a relationship between the two variables. The value of the statistic can range from -1 to 1. Negative values indicate a negative relationship, and positive values indicate a positive relationship. Somers’ d is appropriate when both variables are ordinal, categorical variables.

In [75]:
from scipy.stats import somersd

# Assuming 'data2' is your DataFrame
columns_of_interest = ['Household Income', 'Handset Cost']

# Filter the DataFrame for the selected columns
selected_data = data2[columns_of_interest].dropna()

# Calculate Somers' d in both directions
result1 = somersd(selected_data['Handset Cost'], selected_data['Household Income'])
result2 = somersd(selected_data['Household Income'], selected_data['Handset Cost'])

# Access the Somers' d statistic from the result objects
somersd_statistic1 = result1.statistic
somersd_statistic2 = result2.statistic

# Calculate the average of symmetric Somers' D
symmetric_somersd = (somersd_statistic1 + somersd_statistic2) / 2

# Calculate the standard error of Somers' D
n = len(selected_data)
se_d1 = np.sqrt(2 * (1 - somersd_statistic1 ** 2) / (n * (n - 1)))
se_d2 = np.sqrt(2 * (1 - somersd_statistic2 ** 2) / (n * (n - 1)))
se_ds = np.sqrt(2 * (1 - symmetric_somersd ** 2) / (n * (n - 1)))

# Calculate the asymptotic standard error (ASE) without assuming the null hypothesis
ase_d1 = np.sqrt(2 * (1 - somersd_statistic1 ** 2) / n)
ase_d2 = np.sqrt(2 * (1 - somersd_statistic2 ** 2) / n)
ase_ds = np.sqrt(2 * (1 - symmetric_somersd ** 2) / n)


# Create a list of dictionaries for the table with rounded values
table = [
    {"": "Symmetric Somers' D:", "Value": round(symmetric_somersd, 3), "P-value": None, "Standard Error": round(se_ds, 3), "Asymptotic Standard Error": round(ase_ds, 3)},
    {"": "Handset Cost Dependent Somers' D:", "Value": round(somersd_statistic2, 3), "P-value": round(result2.pvalue, 3), "Standard Error": round(se_d2, 3), "Asymptotic Standard Error": round(ase_d2, 3)},
    {"": "Income Dependent Somers' D:", "Value": round(somersd_statistic1, 3), "P-value": round(result1.pvalue, 3), "Standard Error": round(se_d1, 3), "Asymptotic Standard Error": round(ase_d1, 3)},
    
    
]

# Print the table using the tabulate library
print("\nSomers' d Test Results:")
print(tabulate(table, headers="keys", tablefmt="pretty"))
Somers' d Test Results:
+-----------------------------------+-------+---------+----------------+---------------------------+
|                                   | Value | P-value | Standard Error | Asymptotic Standard Error |
+-----------------------------------+-------+---------+----------------+---------------------------+
|       Symmetric Somers' D:        | 0.283 |         |     0.003      |           0.059           |
| Handset Cost Dependent Somers' D: | 0.279 |   0.0   |     0.003      |           0.059           |
|    Income Dependent Somers' D:    | 0.288 |   0.0   |     0.003      |           0.059           |
+-----------------------------------+-------+---------+----------------+---------------------------+

2.5 INCOME × EXPENDITURE¶

It might be argued that INCOME forms a series of strata, so that it should be considered as a factor. A similar statement can be made with respect to the COST as well. Since INCOME and EXPENDITURE of the mobile were queried simultaneously, we will treat both of them as random. The usual assumptions lead to a multinomial distribution with I×J levels. That is, let Yij denote the count in the (i, j) cell for a sample of size n. Then, the multinomial parameters are pij , which are the probabilities of a randomly selected individual being at level i of the first response and level j of the second response. Under this model there is only one constraint, which can be written in two ways: image.png

The corresponding null hypothesis is that the variables are independent. This means that the probabilities of the joint events in the cells can be written in terms of the marginal probabilities. That is, image-2.png

This hypothesis is to tested against the alternative hypothesis image-3.png

The income groups and the expenditures of the mobile phones are coded as follows:

Income Groups Code
< 5,000 1
5,000 - 10,000 2
10,000 - 15,000 3
> 15,000 4
Monthly Expenditure Code
< 300 1
300 - 500 2
500 - 1,000 3
> 1,000 4
In [76]:
data2.groupby(['Household Income']).size()
Out[76]:
Household Income
1-<Rs.5000          171
2-Rs.5000-10000     177
3-Rs.10000-15000    111
4->15000             68
dtype: int64
In [77]:
data2.groupby(['Household Income', 'Monthly Mobile Expenditure']).size()
Out[77]:
Household Income  Monthly Mobile Expenditure
1-<Rs.5000        1-<Rs.300                     142
                  2-Rs.300-500                   22
                  3-Rs.500-1000                   4
                  4->Rs.1000                      3
2-Rs.5000-10000   1-<Rs.300                     117
                  2-Rs.300-500                   50
                  3-Rs.500-1000                   3
                  4->Rs.1000                      7
3-Rs.10000-15000  1-<Rs.300                      70
                  2-Rs.300-500                   24
                  3-Rs.500-1000                  12
                  4->Rs.1000                      5
4->15000          1-<Rs.300                      42
                  2-Rs.300-500                   17
                  3-Rs.500-1000                   6
                  4->Rs.1000                      3
dtype: int64
In [78]:
# Assuming 'data2' is your DataFrame
columns_of_interest = ['Household Income', 'Monthly Mobile Expenditure']

# Filter the DataFrame for the selected columns
selected_data = data2[columns_of_interest].dropna()

# Calculate the percentages within each category
percentages = selected_data.groupby(['Household Income', 'Monthly Mobile Expenditure']).size() / selected_data.groupby(['Household Income']).size() * 100
percentages = percentages.reset_index(name='Percentage')

# Create a grouped bar chart using Seaborn
plt.figure(figsize=(8,6))
ax = sns.barplot(data=percentages, x='Household Income', y='Percentage', hue='Monthly Mobile Expenditure', palette='rainbow')

# Add data labels for each bar
for p in ax.patches:
    ax.annotate(f'{p.get_height():.1f}%', (p.get_x() + p.get_width() / 2., p.get_height()),
                ha='center', va='center', xytext=(0, 10), textcoords='offset points')

# Move the legend outside the plot
ax.legend(loc='upper left', bbox_to_anchor=(1, 1))
    
plt.xlabel('Household Income')
plt.ylabel('Percentage')
plt.title('Distribution of Household Income and Monthly Mobile Expenditure (Percentage)')
plt.show()

From the above graph we can observe that

  • As the Income level increases, the proportion of students using costly handsets increases.
  • As the Income level increases, the proportion of students using low-cost handsets decreases.
  • In Income groups < 5,000 as well as 5,000−10,000, almost 50% of students are using handsets with a cost in the range of 1,500−2,500.

From the above inferences, it seems that there is some association between Income level and Hand-set cost. We will verify this by constructing a Income × Cost cross-table. Our data set has resulted in the following cross-table for Income versus Cost of the mobile.

In [79]:
#The cross table for Income × Cost has resulted in the following:


# Assuming 'data2' is your DataFrame
columns_of_interest = ['Household Income', 'Monthly Mobile Expenditure']

# Filter the DataFrame for the selected columns
selected_data = data2[columns_of_interest].dropna()

# Create a crosstabulation
cross_tab = pd.crosstab(selected_data['Monthly Mobile Expenditure'],selected_data['Household Income']
                        , margins=True, margins_name='Total')

# Display the crosstab
print(cross_tab)
cross_tab
Household Income            1-<Rs.5000  2-Rs.5000-10000  3-Rs.10000-15000  \
Monthly Mobile Expenditure                                                  
1-<Rs.300                          142              117                70   
2-Rs.300-500                        22               50                24   
3-Rs.500-1000                        4                3                12   
4->Rs.1000                           3                7                 5   
Total                              171              177               111   

Household Income            4->15000  Total  
Monthly Mobile Expenditure                   
1-<Rs.300                         42    371  
2-Rs.300-500                      17    113  
3-Rs.500-1000                      6     25  
4->Rs.1000                         3     18  
Total                             68    527  
Out[79]:
Household Income 1-<Rs.5000 2-Rs.5000-10000 3-Rs.10000-15000 4->15000 Total
Monthly Mobile Expenditure
1-<Rs.300 142 117 70 42 371
2-Rs.300-500 22 50 24 17 113
3-Rs.500-1000 4 3 12 6 25
4->Rs.1000 3 7 5 3 18
Total 171 177 111 68 527
In [80]:
# Assuming 'data2' is your DataFrame
columns_of_interest = ['Household Income', 'Monthly Mobile Expenditure']

# Filter the DataFrame for the selected columns
selected_data = data2[columns_of_interest].dropna()

# Create a crosstabulation with observed counts
observed_cross_tab = pd.crosstab(selected_data['Monthly Mobile Expenditure'], selected_data['Household Income'], margins=True, margins_name='Total')

# Calculate expected counts using chi-squared test
_, _, _, expected = chi2_contingency(observed_cross_tab)

# Create a DataFrame for expected counts
expected_cross_tab = pd.DataFrame(expected, columns=observed_cross_tab.columns, index=observed_cross_tab.index)

# Display both observed and expected crosstabs
# print("Observed Counts:")
# print(observed_cross_tab)

# print("\nExpected Counts:")
# print(expected_cross_tab)

# Combine observed and expected counts into a single DataFrame
combined_cross_tab = pd.concat([observed_cross_tab, expected_cross_tab], keys=['Observed', 'Expected'], axis=0)

# Display the combined crosstab
# print(combined_cross_tab)

combined_cross_tab
Out[80]:
Household Income 1-<Rs.5000 2-Rs.5000-10000 3-Rs.10000-15000 4->15000 Total
Monthly Mobile Expenditure
Observed 1-<Rs.300 142.000000 117.000000 70.000000 42.000000 371.0
2-Rs.300-500 22.000000 50.000000 24.000000 17.000000 113.0
3-Rs.500-1000 4.000000 3.000000 12.000000 6.000000 25.0
4->Rs.1000 3.000000 7.000000 5.000000 3.000000 18.0
Total 171.000000 177.000000 111.000000 68.000000 527.0
Expected 1-<Rs.300 120.381404 124.605313 78.142315 47.870968 371.0
2-Rs.300-500 36.666034 37.952562 23.800759 14.580645 113.0
3-Rs.500-1000 8.111954 8.396584 5.265655 3.225806 25.0
4->Rs.1000 5.840607 6.045541 3.791271 2.322581 18.0
Total 171.000000 177.000000 111.000000 68.000000 527.0
In [81]:
# Assuming 'data2' is your DataFrame
columns_of_interest = ['Household Income', 'Monthly Mobile Expenditure']

# Filter the DataFrame for the selected columns
selected_data = data2[columns_of_interest].dropna()

# Create a crosstabulation with observed counts
observed_cross_tab = pd.crosstab(selected_data['Monthly Mobile Expenditure'], selected_data['Household Income'], margins=True, margins_name='Total')

# Calculate expected counts using chi-squared test
_, _, _, expected = chi2_contingency(observed_cross_tab)

# Create DataFrames for observed and expected counts
observed_df = pd.DataFrame(observed_cross_tab.values, columns=observed_cross_tab.columns, index=observed_cross_tab.index)
expected_df = pd.DataFrame(expected, columns=observed_cross_tab.columns, index=observed_cross_tab.index)

# Calculate percentages within each income group
observed_percentages = observed_df.div(observed_df.loc['Total']) * 100
#expected_percentages = expected_df.div(expected_df.loc['Total']) * 100

# Combine observed and expected counts and percentages into a single DataFrame
combined_cross_tab = pd.concat([observed_df, expected_df, observed_percentages], axis=0,
                               keys=['Observed', 'Expected',  'Observed (%)'])

# Display the combined crosstab with percentages
#print(combined_cross_tab)
combined_cross_tab
Out[81]:
Household Income 1-<Rs.5000 2-Rs.5000-10000 3-Rs.10000-15000 4->15000 Total
Monthly Mobile Expenditure
Observed 1-<Rs.300 142.000000 117.000000 70.000000 42.000000 371.000000
2-Rs.300-500 22.000000 50.000000 24.000000 17.000000 113.000000
3-Rs.500-1000 4.000000 3.000000 12.000000 6.000000 25.000000
4->Rs.1000 3.000000 7.000000 5.000000 3.000000 18.000000
Total 171.000000 177.000000 111.000000 68.000000 527.000000
Expected 1-<Rs.300 120.381404 124.605313 78.142315 47.870968 371.000000
2-Rs.300-500 36.666034 37.952562 23.800759 14.580645 113.000000
3-Rs.500-1000 8.111954 8.396584 5.265655 3.225806 25.000000
4->Rs.1000 5.840607 6.045541 3.791271 2.322581 18.000000
Total 171.000000 177.000000 111.000000 68.000000 527.000000
Observed (%) 1-<Rs.300 83.040936 66.101695 63.063063 61.764706 70.398482
2-Rs.300-500 12.865497 28.248588 21.621622 25.000000 21.442125
3-Rs.500-1000 2.339181 1.694915 10.810811 8.823529 4.743833
4->Rs.1000 1.754386 3.954802 4.504505 4.411765 3.415560
Total 100.000000 100.000000 100.000000 100.000000 100.000000
Clearly, there are discrepancies between the actual frequencies and the expected frequencies. Are these differences statistically significant? To know this, Pearson’s Chi-square test is conducted, and the results are presented in the following table:¶
In [82]:
# Assuming 'data2' is your DataFrame
columns_of_interest = ['Household Income', 'Monthly Mobile Expenditure']

# Filter the DataFrame for the selected columns
selected_data = data2[columns_of_interest].dropna()

# Create a crosstabulation with observed counts
observed_cross_tab = pd.crosstab(selected_data['Monthly Mobile Expenditure'], selected_data['Household Income'])

# Perform chi-squared test
chi2_stat, p_value, dof, expected = chi2_contingency(observed_cross_tab)

# Calculate minimum expected count
min_expected_count = np.min(expected)

# Count cells with expected count less than 5
cells_with_low_expected_count = np.sum(expected < 5)
cells_with_low_expected_count_pct=cells_with_low_expected_count/(observed_cross_tab.size)

# Display chi-square test results in a table
table = [
    ["Pearson Chi-Square", chi2_stat],
    ["Likelihood Ratio", chi2_contingency(observed_cross_tab, lambda_='log-likelihood')[0]],
    ["Linear-by-Linear Association", chi2_contingency(observed_cross_tab, correction=True)[0]],
    ["N of Valid Cases", selected_data.shape[0]],
    ["df", dof],
    ["Asymp. Sig (2-sided) - P-value", p_value],
    ["Minimum Expected Count", min_expected_count],
    [f"{cells_with_low_expected_count} cells ({cells_with_low_expected_count_pct}%) have expected count less than 5."]
]

print("\nChi-Square Test Results:")
print(tabulate(table, headers=[ "Value"]))
Chi-Square Test Results:
                                                            Value
--------------------------------------------------  -------------
Pearson Chi-Square                                   34.6751
Likelihood Ratio                                     34.2285
Linear-by-Linear Association                         34.6751
N of Valid Cases                                    527
df                                                    9
Asymp. Sig (2-sided) - P-value                        6.79791e-05
Minimum Expected Count                                2.32258
3 cells (0.1875%) have expected count less than 5.

From the table we note that the p − value < 0.05 and hence the assumption of independence between the classification variables is rejected. Thus, there is some dependence between the factor levels of Income and Cost. Our next problem is to find out the strength of the association between the factor levels of Income and Cost of the mobile.

To measure the strength of association between categorical variables, SPSS offers several measures of association. These measures are grouped into two categories namely nominal and ordinal. Note that both the classification variables in the above crosstable are ordinal in nature. Thus, it is appropriate to use ordinal measure of association to assess the strength. Somers’ d is one such measure. Somers’ d is an ordinal directional measure that indicates the significance, strength and direction of the relationship between the row and column variables of a cross tabulation. A low significance value (typically less than 0.05) indicates that there is a relationship between the two variables. The value of the statistic can range from -1 to 1. Negative values indicate a negative relationship, and positive values indicate a positive relationship. Somers’ d is appropriate when both variables are ordinal, categorical variables.

In [83]:
from scipy.stats import somersd

# Assuming 'data2' is your DataFrame
columns_of_interest = ['Household Income', 'Monthly Mobile Expenditure']

# Filter the DataFrame for the selected columns
selected_data = data2[columns_of_interest].dropna()

# Calculate Somers' d in both directions
result1 = somersd(selected_data['Monthly Mobile Expenditure'], selected_data['Household Income'])
result2 = somersd(selected_data['Household Income'], selected_data['Monthly Mobile Expenditure'])

# Access the Somers' d statistic from the result objects
somersd_statistic1 = result1.statistic
somersd_statistic2 = result2.statistic

# Calculate the average of symmetric Somers' D
symmetric_somersd = (somersd_statistic1 + somersd_statistic2) / 2

# Calculate the standard error of Somers' D
n = len(selected_data)
se_d1 = np.sqrt(2 * (1 - somersd_statistic1 ** 2) / (n * (n - 1)))
se_d2 = np.sqrt(2 * (1 - somersd_statistic2 ** 2) / (n * (n - 1)))
se_ds = np.sqrt(2 * (1 - symmetric_somersd ** 2) / (n * (n - 1)))

# Calculate the asymptotic standard error (ASE) without assuming the null hypothesis
ase_d1 = np.sqrt(2 * (1 - somersd_statistic1 ** 2) / n)
ase_d2 = np.sqrt(2 * (1 - somersd_statistic2 ** 2) / n)
ase_ds = np.sqrt(2 * (1 - symmetric_somersd ** 2) / n)


# Create a list of dictionaries for the table with rounded values
table = [
    {"": "Symmetric Somers' D:", "Value": round(symmetric_somersd, 3), "P-value": None, "Standard Error": round(se_ds, 3), "Asymptotic Standard Error": round(ase_ds, 3)},
    {"": "Handset Cost Dependent Somers' D:", "Value": round(somersd_statistic2, 3), "P-value": round(result2.pvalue, 3), "Standard Error": round(se_d2, 3), "Asymptotic Standard Error": round(ase_d2, 3)},
    {"": "Income Dependent Somers' D:", "Value": round(somersd_statistic1, 3), "P-value": round(result1.pvalue, 3), "Standard Error": round(se_d1, 3), "Asymptotic Standard Error": round(ase_d1, 3)},
    
    
]

# Print the table using the tabulate library
print("\nSomers' d Test Results:")
print(tabulate(table, headers="keys", tablefmt="pretty"))
Somers' d Test Results:
+-----------------------------------+-------+---------+----------------+---------------------------+
|                                   | Value | P-value | Standard Error | Asymptotic Standard Error |
+-----------------------------------+-------+---------+----------------+---------------------------+
|       Symmetric Somers' D:        | 0.174 |         |     0.003      |           0.061           |
| Handset Cost Dependent Somers' D: | 0.135 |   0.0   |     0.003      |           0.061           |
|    Income Dependent Somers' D:    | 0.213 |   0.0   |     0.003      |           0.06            |
+-----------------------------------+-------+---------+----------------+---------------------------+

Since expected frequencies for 3-Rs.500-1000 and 4->Rs.1000 is less than 5, we decided to group these into 1 single group¶

By Adding a new column¶

In [84]:
data2["Monthly Mobile Expenditure 2"]=data2["Monthly Mobile Expenditure"]
# Your conditions
condition1 = (data2['Monthly Mobile Expenditure 2'] == '3-Rs.500-1000')

# Update values in the specified column based on the conditions
data2.loc[condition1, 'Monthly Mobile Expenditure 2'] = '4->Rs.1000'  # Replace 'updated_value' with the value you want to set

2.5 INCOME × EXPENDITURE¶

It might be argued that INCOME forms a series of strata, so that it should be considered as a factor. A similar statement can be made with respect to the COST as well. Since INCOME and EXPENDITURE of the mobile were queried simultaneously, we will treat both of them as random. The usual assumptions lead to a multinomial distribution with I×J levels. That is, let Yij denote the count in the (i, j) cell for a sample of size n. Then, the multinomial parameters are pij , which are the probabilities of a randomly selected individual being at level i of the first response and level j of the second response. Under this model there is only one constraint, which can be written in two ways: image.png

The corresponding null hypothesis is that the variables are independent. This means that the probabilities of the joint events in the cells can be written in terms of the marginal probabilities. That is, image-2.png

This hypothesis is to tested against the alternative hypothesis image-3.png

The income groups and the expenditures of the mobile phones are coded as follows:

Income Groups Code
< 5,000 1
5,000 - 10,000 2
10,000 - 15,000 3
> 15,000 4
Monthly Expenditure Code
< 300 1
300 - 500 2
500 - 1,000 3
> 1,000 4
In [85]:
data2.groupby(['Household Income']).size()
Out[85]:
Household Income
1-<Rs.5000          171
2-Rs.5000-10000     177
3-Rs.10000-15000    111
4->15000             68
dtype: int64
In [86]:
data2.groupby(['Household Income', 'Monthly Mobile Expenditure 2']).size()
Out[86]:
Household Income  Monthly Mobile Expenditure 2
1-<Rs.5000        1-<Rs.300                       142
                  2-Rs.300-500                     22
                  3-Rs.500-1000                     0
                  4->Rs.1000                        7
2-Rs.5000-10000   1-<Rs.300                       117
                  2-Rs.300-500                     50
                  3-Rs.500-1000                     0
                  4->Rs.1000                       10
3-Rs.10000-15000  1-<Rs.300                        70
                  2-Rs.300-500                     24
                  3-Rs.500-1000                     0
                  4->Rs.1000                       17
4->15000          1-<Rs.300                        42
                  2-Rs.300-500                     17
                  3-Rs.500-1000                     0
                  4->Rs.1000                        9
dtype: int64
In [87]:
# Assuming 'data2' is your DataFrame
columns_of_interest = ['Household Income', 'Monthly Mobile Expenditure 2']

# Filter the DataFrame for the selected columns
selected_data = data2[columns_of_interest].dropna()

# Calculate the percentages within each category
percentages = selected_data.groupby(['Household Income', 'Monthly Mobile Expenditure 2']).size() / selected_data.groupby(['Household Income']).size() * 100
percentages = percentages.reset_index(name='Percentage')

# Create a grouped bar chart using Seaborn
plt.figure(figsize=(8,6))
ax = sns.barplot(data=percentages, x='Household Income', y='Percentage', hue='Monthly Mobile Expenditure 2', palette='rainbow')

# Add data labels for each bar
for p in ax.patches:
    ax.annotate(f'{p.get_height():.1f}%', (p.get_x() + p.get_width() / 2., p.get_height()),
                ha='center', va='center', xytext=(0, 10), textcoords='offset points')

# Move the legend outside the plot
ax.legend(loc='upper left', bbox_to_anchor=(1, 1))
    
plt.xlabel('Household Income')
plt.ylabel('Percentage')
plt.title('Distribution of Household Income and Monthly Mobile Expenditure (Percentage)')
plt.show()

From the above graph we can observe that

  • As the Income level increases, the proportion of students using costly handsets increases.
  • As the Income level increases, the proportion of students using low-cost handsets decreases.
  • In Income groups < 5,000 as well as 5,000−10,000, almost 50% of students are using handsets with a cost in the range of 1,500−2,500.

From the above inferences, it seems that there is some association between Income level and Hand-set cost. We will verify this by constructing a Income × Cost cross-table. Our data set has resulted in the following cross-table for Income versus Cost of the mobile.

In [88]:
#The cross table for Income × Cost has resulted in the following:


# Assuming 'data2' is your DataFrame
columns_of_interest = ['Household Income', 'Monthly Mobile Expenditure 2']

# Filter the DataFrame for the selected columns
selected_data = data2[columns_of_interest].dropna()

# Create a crosstabulation
cross_tab = pd.crosstab(selected_data['Monthly Mobile Expenditure 2'],selected_data['Household Income']
                        , margins=True, margins_name='Total')

# Display the crosstab
print(cross_tab)
cross_tab
Household Income              1-<Rs.5000  2-Rs.5000-10000  3-Rs.10000-15000  \
Monthly Mobile Expenditure 2                                                  
1-<Rs.300                            142              117                70   
2-Rs.300-500                          22               50                24   
4->Rs.1000                             7               10                17   
Total                                171              177               111   

Household Income              4->15000  Total  
Monthly Mobile Expenditure 2                   
1-<Rs.300                           42    371  
2-Rs.300-500                        17    113  
4->Rs.1000                           9     43  
Total                               68    527  
Out[88]:
Household Income 1-<Rs.5000 2-Rs.5000-10000 3-Rs.10000-15000 4->15000 Total
Monthly Mobile Expenditure 2
1-<Rs.300 142 117 70 42 371
2-Rs.300-500 22 50 24 17 113
4->Rs.1000 7 10 17 9 43
Total 171 177 111 68 527
In [89]:
# Assuming 'data2' is your DataFrame
columns_of_interest = ['Household Income', 'Monthly Mobile Expenditure 2']

# Filter the DataFrame for the selected columns
selected_data = data2[columns_of_interest].dropna()

# Create a crosstabulation with observed counts
observed_cross_tab = pd.crosstab(selected_data['Monthly Mobile Expenditure 2'], selected_data['Household Income'], margins=True, margins_name='Total')

# Calculate expected counts using chi-squared test
_, _, _, expected = chi2_contingency(observed_cross_tab)

# Create a DataFrame for expected counts
expected_cross_tab = pd.DataFrame(expected, columns=observed_cross_tab.columns, index=observed_cross_tab.index)

# Display both observed and expected crosstabs
# print("Observed Counts:")
# print(observed_cross_tab)

# print("\nExpected Counts:")
# print(expected_cross_tab)

# Combine observed and expected counts into a single DataFrame
combined_cross_tab = pd.concat([observed_cross_tab, expected_cross_tab], keys=['Observed', 'Expected'], axis=0)

# Display the combined crosstab
# print(combined_cross_tab)

combined_cross_tab
Out[89]:
Household Income 1-<Rs.5000 2-Rs.5000-10000 3-Rs.10000-15000 4->15000 Total
Monthly Mobile Expenditure 2
Observed 1-<Rs.300 142.000000 117.000000 70.000000 42.000000 371.0
2-Rs.300-500 22.000000 50.000000 24.000000 17.000000 113.0
4->Rs.1000 7.000000 10.000000 17.000000 9.000000 43.0
Total 171.000000 177.000000 111.000000 68.000000 527.0
Expected 1-<Rs.300 120.381404 124.605313 78.142315 47.870968 371.0
2-Rs.300-500 36.666034 37.952562 23.800759 14.580645 113.0
4->Rs.1000 13.952562 14.442125 9.056926 5.548387 43.0
Total 171.000000 177.000000 111.000000 68.000000 527.0
In [90]:
# Assuming 'data2' is your DataFrame
columns_of_interest = ['Household Income', 'Monthly Mobile Expenditure 2']

# Filter the DataFrame for the selected columns
selected_data = data2[columns_of_interest].dropna()

# Create a crosstabulation with observed counts
observed_cross_tab = pd.crosstab(selected_data['Monthly Mobile Expenditure 2'], selected_data['Household Income'], margins=True, margins_name='Total')

# Calculate expected counts using chi-squared test
_, _, _, expected = chi2_contingency(observed_cross_tab)

# Create DataFrames for observed and expected counts
observed_df = pd.DataFrame(observed_cross_tab.values, columns=observed_cross_tab.columns, index=observed_cross_tab.index)
expected_df = pd.DataFrame(expected, columns=observed_cross_tab.columns, index=observed_cross_tab.index)

# Calculate percentages within each income group
observed_percentages = observed_df.div(observed_df.loc['Total']) * 100
#expected_percentages = expected_df.div(expected_df.loc['Total']) * 100

# Combine observed and expected counts and percentages into a single DataFrame
combined_cross_tab = pd.concat([observed_df, expected_df, observed_percentages], axis=0,
                               keys=['Observed', 'Expected',  'Observed (%)'])

# Display the combined crosstab with percentages
#print(combined_cross_tab)
combined_cross_tab
Out[90]:
Household Income 1-<Rs.5000 2-Rs.5000-10000 3-Rs.10000-15000 4->15000 Total
Monthly Mobile Expenditure 2
Observed 1-<Rs.300 142.000000 117.000000 70.000000 42.000000 371.000000
2-Rs.300-500 22.000000 50.000000 24.000000 17.000000 113.000000
4->Rs.1000 7.000000 10.000000 17.000000 9.000000 43.000000
Total 171.000000 177.000000 111.000000 68.000000 527.000000
Expected 1-<Rs.300 120.381404 124.605313 78.142315 47.870968 371.000000
2-Rs.300-500 36.666034 37.952562 23.800759 14.580645 113.000000
4->Rs.1000 13.952562 14.442125 9.056926 5.548387 43.000000
Total 171.000000 177.000000 111.000000 68.000000 527.000000
Observed (%) 1-<Rs.300 83.040936 66.101695 63.063063 61.764706 70.398482
2-Rs.300-500 12.865497 28.248588 21.621622 25.000000 21.442125
4->Rs.1000 4.093567 5.649718 15.315315 13.235294 8.159393
Total 100.000000 100.000000 100.000000 100.000000 100.000000
Clearly, there are discrepancies between the actual frequencies and the expected frequencies. Are these differences statistically significant? To know this, Pearson’s Chi-square test is conducted, and the results are presented in the following table:¶
In [91]:
# Assuming 'data2' is your DataFrame
columns_of_interest = ['Household Income', 'Monthly Mobile Expenditure 2']

# Filter the DataFrame for the selected columns
selected_data = data2[columns_of_interest].dropna()

# Create a crosstabulation with observed counts
observed_cross_tab = pd.crosstab(selected_data['Monthly Mobile Expenditure 2'], selected_data['Household Income'])

# Perform chi-squared test
chi2_stat, p_value, dof, expected = chi2_contingency(observed_cross_tab)

# Calculate minimum expected count
min_expected_count = np.min(expected)

# Count cells with expected count less than 5
cells_with_low_expected_count = np.sum(expected < 5)
cells_with_low_expected_count_pct=cells_with_low_expected_count/(observed_cross_tab.size)

# Display chi-square test results in a table
table = [
    ["Pearson Chi-Square", chi2_stat],
    ["Likelihood Ratio", chi2_contingency(observed_cross_tab, lambda_='log-likelihood')[0]],
    ["Linear-by-Linear Association", chi2_contingency(observed_cross_tab, correction=True)[0]],
    ["N of Valid Cases", selected_data.shape[0]],
    ["df", dof],
    ["Asymp. Sig (2-sided) - P-value", p_value],
    ["Minimum Expected Count", min_expected_count],
    [f"{cells_with_low_expected_count} cells ({cells_with_low_expected_count_pct}%) have expected count less than 5."]
]

print("\nChi-Square Test Results:")
print(tabulate(table, headers=[ "Value"]))
Chi-Square Test Results:
                                                         Value
-----------------------------------------------  -------------
Pearson Chi-Square                                29.9528
Likelihood Ratio                                  29.5948
Linear-by-Linear Association                      29.9528
N of Valid Cases                                 527
df                                                 6
Asymp. Sig (2-sided) - P-value                     4.01283e-05
Minimum Expected Count                             5.54839
0 cells (0.0%) have expected count less than 5.

From the table we note that the p − value < 0.05 and hence the assumption of independence between the classification variables is rejected. Thus, there is some dependence between the factor levels of Income and Cost. Our next problem is to find out the strength of the association between the factor levels of Income and Cost of the mobile.

To measure the strength of association between categorical variables, SPSS offers several measures of association. These measures are grouped into two categories namely nominal and ordinal. Note that both the classification variables in the above crosstable are ordinal in nature. Thus, it is appropriate to use ordinal measure of association to assess the strength. Somers’ d is one such measure. Somers’ d is an ordinal directional measure that indicates the significance, strength and direction of the relationship between the row and column variables of a cross tabulation. A low significance value (typically less than 0.05) indicates that there is a relationship between the two variables. The value of the statistic can range from -1 to 1. Negative values indicate a negative relationship, and positive values indicate a positive relationship. Somers’ d is appropriate when both variables are ordinal, categorical variables.

In [92]:
from scipy.stats import somersd

# Assuming 'data2' is your DataFrame
columns_of_interest = ['Household Income', 'Monthly Mobile Expenditure 2']

# Filter the DataFrame for the selected columns
selected_data = data2[columns_of_interest].dropna()

# Calculate Somers' d in both directions
result1 = somersd(selected_data['Monthly Mobile Expenditure 2'], selected_data['Household Income'])
result2 = somersd(selected_data['Household Income'], selected_data['Monthly Mobile Expenditure 2'])

# Access the Somers' d statistic from the result objects
somersd_statistic1 = result1.statistic
somersd_statistic2 = result2.statistic

# Calculate the average of symmetric Somers' D
symmetric_somersd = (somersd_statistic1 + somersd_statistic2) / 2

# Calculate the standard error of Somers' D
n = len(selected_data)
se_d1 = np.sqrt(2 * (1 - somersd_statistic1 ** 2) / (n * (n - 1)))
se_d2 = np.sqrt(2 * (1 - somersd_statistic2 ** 2) / (n * (n - 1)))
se_ds = np.sqrt(2 * (1 - symmetric_somersd ** 2) / (n * (n - 1)))

# Calculate the asymptotic standard error (ASE) without assuming the null hypothesis
ase_d1 = np.sqrt(2 * (1 - somersd_statistic1 ** 2) / n)
ase_d2 = np.sqrt(2 * (1 - somersd_statistic2 ** 2) / n)
ase_ds = np.sqrt(2 * (1 - symmetric_somersd ** 2) / n)


# Create a list of dictionaries for the table with rounded values
table = [
    {"": "Symmetric Somers' D:", "Value": round(symmetric_somersd, 3), "P-value": None, "Standard Error": round(se_ds, 3), "Asymptotic Standard Error": round(ase_ds, 3)},
    {"": "Handset Cost Dependent Somers' D:", "Value": round(somersd_statistic2, 3), "P-value": round(result2.pvalue, 3), "Standard Error": round(se_d2, 3), "Asymptotic Standard Error": round(ase_d2, 3)},
    {"": "Income Dependent Somers' D:", "Value": round(somersd_statistic1, 3), "P-value": round(result1.pvalue, 3), "Standard Error": round(se_d1, 3), "Asymptotic Standard Error": round(ase_d1, 3)},
    
    
]

# Print the table using the tabulate library
print("\nSomers' d Test Results:")
print(tabulate(table, headers="keys", tablefmt="pretty"))
Somers' d Test Results:
+-----------------------------------+-------+---------+----------------+---------------------------+
|                                   | Value | P-value | Standard Error | Asymptotic Standard Error |
+-----------------------------------+-------+---------+----------------+---------------------------+
|       Symmetric Somers' D:        | 0.176 |         |     0.003      |           0.061           |
| Handset Cost Dependent Somers' D: | 0.136 |   0.0   |     0.003      |           0.061           |
|    Income Dependent Somers' D:    | 0.216 |   0.0   |     0.003      |           0.06            |
+-----------------------------------+-------+---------+----------------+---------------------------+

Chapter 3¶

Analysis of Handset Selection¶

3.1 Introduction¶

Factor Analysis is a popular dimension reduction technique. Using which we can study the correlation structure among the variables. Factor analysis partitions the manifest variables in to groups and each partition signifies the effect of a latent variable called common factor. These new variables stand for constructs that cannot be directly measurable. Such an analysis is vital in different fields of research. For example, in a marketing research, companies spends crores of rupees towards the advertisement of their products. Factor analysis may be used in order to know whether their spending towards advertisement is really worth.

Objectives¶

  • What are the factors that influence a student in the selection of a mobile phone?
  • In the selection of a handset, are the influencing factors the same for rural as well as urban student population?
  • In the selection of a handset, is there any gender difference among the influencing factors?
  • What for students are using mobile phones?

In section-2 of this Chapter, we will analyze the factors influencing the selection of a handset and Section-3 describes what for a student is using a mobile.

3.2 Factors Influencing in the Handset Selection¶

Our sample consists of 527 students selected from four categories namely intermediate, graduate, post-graduate and research scholars. The category graduate includes students from usual 3-year degree courses as well as professional courses like engineering. Similarly, the post-graduate category includes engineering students. One of our objectives is to know about the factors that actually influence students in the selection of their mobiles. For this purpose we have asked the sampling units to rate several variables on a 5-point semantic Likert-scale. The data so obtained is analyzed, in this section, using Factor procedure of SPSS package.

3.2.1 Data Screening¶

Given a set of variables, SPSS will nearly always find a factor solution to that set of variables. The solution will not have any real meaning if the variables analyzed are not sensible. There are several techniques that can be used to know whether we can proceed with the factor analyzing the data set. Some of the popular procedures one can adopt are:

  • Study correlations among the variables.
  • Anti-Image Matrix.
  • Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
  • Bartlett’s Test of Sphericity.
  • Cluster Analysis.

3.2.2 Correlation Table:¶

For the purpose of knowing the influencing factors in the selection of handsets among student population, we have included 21 variables in our project(see the Appendix). We have obtained the correlation matrix for all the 21 variables. There are 8 variables left after screening the unimportant variables. These correlations are presented in the Correlation Table. This table also shows the groups of variables on the basis of correlations with other variables. Note that the correlation table also outputs the determinant value. This option is vital for testing multicollinearity or singularity. The determinant should be greater than 0.00001.

When there are large number of variables, inspecting the correlation table is clumsy. In such cases, one can resort to Matrix-Scatter Plot to asses the overall correlations of various variables with the other variables. As the variables are measured on Likert Scale, this option is not useful in present problem.

In [93]:
data2.columns
Out[93]:
Index(['Record_ID', 'Age', 'Gender', 'Education', 'Father Occupation',
       'Household Income', 'Native (Rural/Urban)',
       'Handset Type(Color/ Black & white)', 'Handset Brand',
       'Handset Selection Reason', 'Handset Cost', 'Handset Age',
       'Calls Receiving per day', 'SMS Receiving per day',
       'SMS Sending per day', 'Frequent Communication Type',
       'Feel safe and secure with mobile', 'Monthly Mobile Expenditure',
       'Current Service Provider', 'Current Service Provider Age',
       'SP - Influence Rating for Signal',
       'SP - Influence Rating for Customer care',
       'SP - Influence Rating for Electronic media',
       'SP - Influence Rating for Hoardings',
       'SP - Influence Rating for Friends',
       'SP - Influence Rating for Family members',
       'SP - Influence Rating for Print media',
       'SP - Influence Rating for Call costs',
       'HS - Influence Rating for Brand',
       'HS - Influence Rating for Battery back up',
       'HS - Influence Rating for Keypad', 'HS - Influence Rating for Display',
       'HS - Influence Rating for Sound',
       'HS - Influence Rating for Size of the screen',
       'HS - Influence Rating for Electronic media',
       'HS - Influence Rating for Print media',
       'HS - Influence Rating for Hoardings',
       'HS - Influence Rating for Friends',
       'HS - Influence Rating for Family members',
       'HS - Influence Rating for Voice clarity',
       'HS - Influence Rating for MP3 facility',
       'HS - Influence Rating for Bluetooth',
       'HS - Influence Rating for Camera pixels',
       'HS - Influence Rating for FM radio', 'HS - Influence Rating for Games',
       'HS - Influence Rating for Video',
       'HS - Influence Rating for Memory size',
       'HS - Influence Rating for Price', 'HS - Influence Rating for Stylish',
       'Stylish Handset Brand', 'More Models Handset Brand',
       'User friendly Handset Brand', 'Reasonable prices Handset Brand',
       'Added features Handset Brand', 'Customer service Handset Brand',
       'Advertisements Handset Brand', 'Sales promotions Handset Brand',
       'Available service centers Handset Brand', 'Durability Handset Brand',
       'Overall, I like the Handset Brand', 'User friendly Service Provider',
       'Added features Service Provider', 'Customer service Service Provider',
       'Advertisements Service Provider', 'Sales promotions Service Provider',
       'Available service centers Service Provider',
       'Overall, I like Service Provider', 'All my friends use',
       'Old fashioned', 'Prestige', 'Secure', 'Show-off', 'Life style',
       'Communication', 'Facilities', 'Life easier', 'Call Friends and Family',
       'Mostly use SMS', 'Music or Videos', 'Games',
       'Monthly Mobile Expenditure 2'],
      dtype='object', name='Feature')
In [94]:
HS_Influence_Columns = ['HS - Influence Rating for Brand',
                       'HS - Influence Rating for Battery back up',
                       'HS - Influence Rating for Keypad', 
                       'HS - Influence Rating for Display',
                       'HS - Influence Rating for Sound',
                       'HS - Influence Rating for Size of the screen',
                       'HS - Influence Rating for Electronic media',
                       'HS - Influence Rating for Print media',
                       'HS - Influence Rating for Hoardings',
                       'HS - Influence Rating for Friends',
                       'HS - Influence Rating for Family members',
                       'HS - Influence Rating for Voice clarity',
                       'HS - Influence Rating for MP3 facility',
                       'HS - Influence Rating for Bluetooth',
                       'HS - Influence Rating for Camera pixels',
                       'HS - Influence Rating for FM radio', 
                       'HS - Influence Rating for Games',
                       'HS - Influence Rating for Video',
                       'HS - Influence Rating for Memory size',
                       'HS - Influence Rating for Price', 
                       'HS - Influence Rating for Stylish']

data_HS_Factor = data2[HS_Influence_Columns]
data_HS_Factor.columns
Out[94]:
Index(['HS - Influence Rating for Brand',
       'HS - Influence Rating for Battery back up',
       'HS - Influence Rating for Keypad', 'HS - Influence Rating for Display',
       'HS - Influence Rating for Sound',
       'HS - Influence Rating for Size of the screen',
       'HS - Influence Rating for Electronic media',
       'HS - Influence Rating for Print media',
       'HS - Influence Rating for Hoardings',
       'HS - Influence Rating for Friends',
       'HS - Influence Rating for Family members',
       'HS - Influence Rating for Voice clarity',
       'HS - Influence Rating for MP3 facility',
       'HS - Influence Rating for Bluetooth',
       'HS - Influence Rating for Camera pixels',
       'HS - Influence Rating for FM radio', 'HS - Influence Rating for Games',
       'HS - Influence Rating for Video',
       'HS - Influence Rating for Memory size',
       'HS - Influence Rating for Price', 'HS - Influence Rating for Stylish'],
      dtype='object', name='Feature')
In [95]:
data_HS_Factor.columns = data_HS_Factor.columns.str.replace('HS - Influence Rating for ', '')
print(data_HS_Factor.columns)
data_HS_Factor
Index(['Brand', 'Battery back up', 'Keypad', 'Display', 'Sound',
       'Size of the screen', 'Electronic media', 'Print media', 'Hoardings',
       'Friends', 'Family members', 'Voice clarity', 'MP3 facility',
       'Bluetooth', 'Camera pixels', 'FM radio', 'Games', 'Video',
       'Memory size', 'Price', 'Stylish'],
      dtype='object', name='Feature')
Out[95]:
Feature Brand Battery back up Keypad Display Sound Size of the screen Electronic media Print media Hoardings Friends Family members Voice clarity MP3 facility Bluetooth Camera pixels FM radio Games Video Memory size Price Stylish
0 5 4 4 4 3 3 3 2 2 4 4 5 3 3 3 4 4 2 4 5 4
1 3 3 5 4 4 5 4 4 4 5 3 3 1 1 1 5 3 1 2 4 5
2 5 5 4 4 4 4 3 3 3 5 5 4 4 3 3 4 3 3 4 4 4
3 4 4 4 4 3 4 4 3 4 4 4 4 4 4 3 4 4 4 4 4 4
4 5 4 5 5 5 5 5 5 5 4 5 5 5 3 3 5 5 3 3 5 5
5 5 5 4 4 5 4 3 3 3 4 5 4 5 4 4 4 4 4 4 4 4
6 5 5 4 4 4 4 3 3 3 4 4 5 5 5 2 4 4 1 4 1 4
7 5 5 5 5 5 5 3 3 3 5 5 5 4 3 3 3 5 3 4 4 4
8 5 5 5 5 5 4 4 4 4 5 4 5 4 4 4 5 5 5 4 4 4
9 4 5 4 4 3 2 3 3 3 5 5 5 3 1 1 1 2 2 4 4 3
10 5 5 3 3 3 3 4 4 4 4 3 3 4 2 3 4 4 3 4 2 3
11 5 5 4 4 3 3 3 3 3 5 5 4 4 3 3 3 3 3 4 4 4
12 5 5 5 5 5 4 4 4 4 4 4 5 5 5 5 5 5 5 5 4 5
13 5 5 5 5 5 4 4 4 4 5 5 4 4 3 4 4 3 3 4 3 3
14 4 5 5 5 5 4 4 4 3 4 4 5 5 5 5 5 5 5 5 4 4
15 5 4 3 4 4 4 2 2 2 4 5 3 3 3 3 3 3 3 4 5 4
16 3 3 3 5 4 3 4 2 1 3 5 4 1 1 2 3 2 5 2 1 1
17 4 4 4 3 3 3 3 3 3 3 4 4 4 2 3 3 2 4 4 4 3
18 3 4 4 3 2 4 3 3 3 3 2 2 3 4 4 2 2 3 3 4 4
19 3 4 3 3 2 2 4 3 2 2 2 4 1 3 2 3 4 2 1 4 2
20 5 5 5 5 5 5 4 4 4 3 3 5 1 1 1 1 1 1 1 5 5
21 5 5 4 3 3 3 3 3 3 5 3 2 2 2 2 2 2 2 3 4 3
22 4 3 3 3 3 4 3 4 4 4 4 3 3 3 4 4 4 4 4 3 3
23 3 3 4 4 4 3 5 4 4 4 4 3 2 3 4 2 2 5 4 4 4
24 4 5 5 5 3 4 3 5 5 5 5 5 4 4 4 5 3 3 3 5 3
25 4 5 5 5 5 3 3 3 3 3 3 5 4 4 5 5 3 5 5 5 3
26 5 5 4 4 4 4 3 3 3 5 5 4 4 3 3 4 3 3 4 4 4
27 5 5 4 4 4 4 3 3 3 5 5 4 4 3 3 4 3 3 4 4 4
28 5 5 4 4 4 3 3 3 3 5 5 4 3 3 3 4 3 3 4 4 4
29 4 4 3 4 3 2 2 1 3 5 3 1 1 1 1 1 1 1 1 3 1
30 5 5 5 5 5 5 2 2 3 5 5 5 5 5 5 5 5 5 5 4 5
31 5 4 4 4 3 4 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4
32 5 5 3 5 5 5 3 3 3 3 3 4 4 5 4 4 2 4 4 5 5
33 5 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
34 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 2
35 5 4 3 5 4 3 5 4 3 5 5 4 3 5 3 5 3 5 4 3 5
36 4 2 3 1 4 2 3 1 2 3 1 4 3 2 4 2 5 2 4 5 3
37 5 4 3 3 4 4 4 4 3 4 4 3 3 3 4 4 4 3 4 5 2
38 5 4 3 5 5 5 4 4 3 3 4 4 5 5 4 4 5 4 3 4 5
39 3 3 3 3 3 3 3 2 4 4 4 2 2 1 3 3 3 4 4 5 5
40 1 2 3 3 3 2 2 1 4 4 2 3 3 5 2 2 2 1 2 2 4
41 5 3 2 3 2 2 3 3 3 2 4 2 2 3 2 1 4 3 1 3 1
42 5 4 5 4 5 4 5 4 5 4 5 4 2 3 4 5 4 3 4 5 4
43 3 4 4 3 3 3 3 3 3 3 3 3 3 2 3 2 3 4 3 2 3
44 5 5 4 4 4 4 5 3 4 1 3 5 3 2 4 4 3 2 3 4 2
45 4 4 4 5 3 5 3 3 3 4 2 5 5 3 3 3 3 2 4 4 4
46 5 3 3 3 4 2 1 4 4 5 5 5 2 2 2 4 4 1 3 3 3
47 5 4 5 4 4 4 4 5 5 4 4 5 5 5 5 5 5 4 5 4 5
48 5 5 5 3 5 3 4 3 3 4 4 4 2 2 2 4 4 2 3 3 3
49 5 5 5 5 5 4 4 4 4 5 5 3 2 2 2 2 2 4 3 3 2
50 4 4 4 5 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
51 5 4 4 4 4 4 5 4 5 5 5 5 3 1 1 5 4 4 3 3 3
52 5 4 5 3 5 4 5 5 5 5 5 5 4 5 5 5 3 5 5 5 5
53 5 4 3 3 4 3 4 2 5 3 5 4 3 3 2 5 3 3 3 5 5
54 5 4 4 5 4 4 3 5 4 3 5 4 5 3 4 5 4 3 5 4 5
55 5 4 5 4 5 4 4 5 4 3 5 4 5 4 5 4 5 4 5 4 5
56 5 4 5 4 4 3 5 4 3 5 4 5 3 5 4 4 3 4 4 4 5
57 5 4 5 4 5 4 5 4 3 3 2 3 2 3 2 3 2 2 1 2 1
58 5 4 3 5 3 5 2 4 3 5 4 5 3 2 4 5 3 5 4 3 5
59 5 4 3 5 4 4 4 5 2 1 4 3 3 2 1 3 1 1 5 3 3
60 5 5 4 3 5 3 3 3 3 2 4 3 2 4 3 2 5 3 4 3 3
61 5 5 5 5 4 5 3 3 3 4 4 4 3 3 4 4 3 4 3 4 5
62 5 5 5 5 5 4 4 3 3 2 2 1 1 1 1 1 3 3 3 3 3
63 5 5 4 4 3 3 3 3 4 4 5 5 5 5 5 5 4 4 4 4 4
64 5 5 5 4 4 4 4 4 4 4 4 3 5 5 5 5 5 5 4 4 4
65 4 3 3 3 4 4 2 2 2 5 5 5 4 4 1 1 2 2 2 2 2
66 5 4 5 4 3 3 4 2 5 5 5 3 1 1 1 5 5 3 4 3 3
67 5 4 5 4 4 4 5 5 5 5 4 4 4 4 4 4 3 4 3 3 3
68 5 4 5 3 4 3 5 3 5 5 5 3 4 4 4 5 4 5 3 4 5
69 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
70 4 4 4 4 5 4 4 4 4 4 4 5 3 4 4 4 4 4 4 4 4
71 4 5 4 4 4 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4
72 5 5 5 5 5 4 3 3 3 4 4 5 3 3 3 5 5 3 3 4 5
73 4 5 4 5 4 4 4 4 5 5 5 5 3 3 3 5 5 3 5 4 5
74 3 4 4 4 4 4 4 4 4 4 4 4 2 2 2 2 4 2 2 4 4
75 5 5 4 5 4 4 4 3 3 4 5 5 4 4 4 4 4 4 4 5 3
76 5 5 5 5 5 5 5 5 4 5 5 5 4 5 4 5 5 5 3 4 4
77 4 4 3 4 5 4 3 3 2 2 4 4 3 4 3 5 3 3 3 4 5
78 4 5 4 4 4 4 3 2 2 4 4 4 4 4 5 4 2 4 5 5 5
79 3 3 3 3 4 4 3 3 3 4 4 4 4 4 4 4 3 3 3 3 4
80 5 4 3 5 5 4 5 3 4 5 4 4 4 5 4 3 4 5 4 5 3
81 3 4 3 2 5 3 2 3 5 4 3 2 1 2 3 3 3 3 4 4 3
82 5 3 4 1 3 2 3 4 4 3 2 4 3 5 3 3 3 5 5 5 5
83 5 4 5 4 4 4 4 4 4 5 5 4 3 2 1 3 1 1 2 1 4
84 5 4 3 5 3 4 4 3 5 4 4 2 3 4 3 4 3 5 4 4 3
85 5 5 5 5 5 5 4 4 4 4 4 5 4 5 4 4 4 4 5 4 4
86 4 4 4 4 5 5 5 5 5 4 4 5 5 5 5 5 5 5 5 5 5
87 5 3 3 4 3 2 3 3 3 4 4 2 1 1 1 1 1 1 1 4 4
88 4 4 4 4 4 4 3 3 3 4 4 5 5 3 3 5 2 3 4 5 5
89 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
90 3 4 4 4 4 3 3 4 2 4 3 4 3 3 4 4 3 4 3 3 3
91 4 4 3 3 3 4 4 3 4 3 4 3 4 3 4 2 2 2 3 3 2
92 4 4 4 4 3 3 3 2 4 4 3 3 2 2 2 3 4 3 3 3 2
93 5 5 5 5 5 5 5 5 5 5 1 5 1 1 1 5 2 2 2 5 4
94 5 4 5 5 5 4 4 4 4 3 3 3 3 5 3 5 3 3 4 4 5
95 5 4 4 4 4 4 3 3 3 4 4 4 2 2 2 2 2 2 3 3 3
96 4 3 3 4 4 4 4 2 2 4 4 4 2 2 2 4 4 2 5 4 4
97 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
98 5 4 4 5 5 3 5 3 3 5 3 5 3 3 2 4 5 3 4 3 3
99 4 2 4 4 4 3 3 4 4 4 3 3 2 3 3 4 4 3 3 3 3
100 5 5 4 4 4 4 4 3 3 3 5 4 4 4 4 3 3 4 3 5 4
101 5 3 5 5 5 5 4 4 4 4 4 5 5 5 5 5 5 5 5 3 5
102 5 4 4 4 3 4 3 3 3 3 3 3 3 3 3 4 4 3 4 3 4
103 5 5 3 5 3 5 2 2 2 5 5 5 5 5 5 5 5 5 5 3 5
104 4 4 2 4 4 2 4 4 4 4 4 4 4 2 2 2 2 2 2 5 2
105 5 4 3 3 3 3 2 3 2 4 4 3 3 3 3 4 3 3 3 4 3
106 4 4 3 3 4 4 4 4 3 4 3 3 3 3 4 4 4 4 3 4 4
107 4 4 5 5 5 4 4 4 4 4 4 4 3 3 3 3 4 3 4 4 4
108 5 4 4 5 5 4 4 4 3 4 4 5 4 2 2 3 4 2 2 3 4
109 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
110 4 4 4 4 4 4 3 3 3 4 4 4 3 3 3 3 2 4 4 3 4
111 4 4 4 4 4 4 4 4 3 4 4 4 3 4 4 4 4 3 3 3 3
112 5 2 5 5 4 5 4 4 3 4 5 4 4 4 4 3 3 5 5 5 5
113 4 4 3 5 5 4 4 4 4 4 4 4 5 4 5 4 4 4 4 4 4
114 5 5 4 4 4 4 4 3 3 5 5 4 5 5 5 5 5 5 4 4 5
115 4 4 4 4 4 3 3 3 4 3 4 3 4 1 3 4 4 4 4 2 3
116 4 5 4 5 5 5 4 3 3 4 4 5 5 5 5 5 5 5 5 5 5
117 4 5 4 5 5 5 5 4 4 5 4 5 5 5 5 5 4 5 5 5 5
118 5 5 4 5 5 4 4 4 4 4 5 5 5 5 5 4 4 4 4 5 4
119 5 5 4 5 5 5 5 5 4 4 4 5 5 5 5 5 4 5 5 5 5
120 4 4 5 3 4 3 3 3 2 5 5 3 2 2 2 4 3 2 3 4 4
121 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
122 5 4 4 4 3 5 5 4 4 5 4 4 4 4 5 5 3 4 4 3 3
123 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
124 5 5 4 5 4 3 3 3 4 2 2 4 4 3 4 4 1 4 4 3 4
125 5 5 4 4 5 4 3 3 4 5 4 4 4 4 5 4 5 5 5 4 5
126 4 4 3 3 3 4 3 3 3 4 4 4 4 3 3 4 3 4 3 3 3
127 4 5 3 3 4 5 2 1 1 3 5 5 1 1 2 2 2 2 2 5 2
128 5 4 5 5 5 5 5 5 5 5 4 5 2 2 2 2 4 2 2 4 2
129 5 5 5 5 5 3 2 2 4 5 3 5 2 2 2 2 2 2 2 4 4
130 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
131 4 5 3 5 4 4 4 3 3 4 3 3 5 4 5 5 3 4 4 5 5
132 5 5 4 4 5 4 3 3 4 5 5 4 2 5 5 5 5 4 4 4 4
133 4 5 5 5 5 4 3 3 3 4 3 5 2 4 5 4 2 5 5 5 5
134 4 4 4 5 5 5 3 3 2 3 4 4 5 5 5 4 2 5 4 4 5
135 5 5 5 5 5 5 4 4 1 5 4 5 1 1 1 5 3 1 1 5 5
136 5 5 4 5 4 4 4 3 3 4 3 5 5 5 5 5 5 5 5 5 5
137 4 4 4 4 4 3 3 3 3 4 3 4 3 3 3 4 3 3 5 3 3
138 5 4 5 5 5 5 4 4 3 3 3 5 4 3 3 4 4 4 5 5 5
139 5 5 4 4 3 5 4 3 3 4 4 4 4 4 5 4 2 4 5 3 5
140 5 5 5 5 3 2 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3
141 5 5 5 5 4 4 3 3 3 4 4 4 3 3 3 3 4 3 3 5 4
142 5 5 4 4 3 5 3 3 3 5 3 5 3 2 2 5 5 5 5 5 5
143 4 4 4 4 4 4 4 3 3 3 3 4 4 3 3 5 3 4 5 5 5
144 4 4 4 5 5 5 3 3 3 3 3 5 5 4 4 5 3 4 5 5 5
145 5 5 5 5 5 5 3 3 3 3 3 5 5 5 5 5 4 5 5 5 5
146 5 5 5 5 5 4 4 4 4 4 4 4 1 1 1 5 5 2 2 5 5
147 5 5 5 5 5 5 4 4 4 5 4 5 4 4 5 5 4 4 5 5 5
148 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
149 3 5 5 5 5 3 3 3 3 3 3 5 2 2 2 2 2 2 2 2 2
150 5 5 5 5 5 4 4 3 3 5 5 3 3 3 3 3 4 3 3 4 4
151 4 4 4 4 4 4 2 2 2 4 4 4 2 2 2 2 2 2 2 5 4
152 5 5 5 5 5 4 4 4 4 5 5 5 5 5 5 4 4 5 5 5 5
153 4 4 4 4 3 3 3 3 3 4 3 4 3 3 3 4 3 4 4 3 4
154 5 5 3 3 4 5 3 4 5 5 4 3 2 1 4 5 3 4 5 3 4
155 3 5 4 5 5 5 4 4 4 4 4 5 4 4 3 4 3 3 4 5 4
156 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 5 3 4 5 5 3
157 4 4 4 4 4 4 4 3 3 4 4 4 4 2 2 4 3 4 4 4 3
158 4 4 4 4 4 4 4 4 4 2 4 4 2 2 2 4 2 2 2 5 4
159 3 4 4 4 3 4 4 4 4 4 3 3 4 3 3 4 3 3 4 4 4
160 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
161 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
162 5 5 5 5 5 5 4 4 4 5 5 5 5 5 5 4 5 5 5 5 5
163 5 5 5 5 4 4 4 4 5 5 4 5 5 5 5 4 5 5 5 5 5
164 5 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
165 4 5 5 5 5 5 4 4 4 5 5 4 5 5 5 5 4 5 5 4 5
166 5 5 5 5 4 5 5 5 3 5 4 5 5 5 5 5 5 5 5 5 5
167 5 5 3 5 5 3 3 3 3 4 5 5 5 5 4 4 5 5 5 5 5
168 5 5 5 5 5 5 5 5 5 5 5 2 1 2 2 2 4 3 4 3 5
169 5 5 4 5 5 5 4 4 4 4 4 4 5 4 5 4 4 5 4 5 5
170 5 4 4 4 4 4 4 4 4 4 4 4 2 2 2 4 4 2 2 3 3
171 5 4 4 5 3 3 4 3 3 4 4 4 4 4 4 5 3 3 2 4 4
172 4 4 5 5 5 5 3 3 4 3 4 5 3 4 4 5 2 2 3 4 4
173 5 5 4 4 4 4 3 3 3 3 4 4 4 5 5 5 5 4 5 4 4
174 5 5 3 4 4 3 3 2 3 4 3 5 5 5 3 5 4 4 5 5 5
175 5 5 4 5 4 5 5 5 5 5 5 5 2 2 2 5 5 2 2 5 5
176 5 4 5 4 5 4 5 5 5 4 4 5 5 4 4 5 5 4 5 4 5
177 5 4 4 4 4 4 3 4 4 3 5 5 2 3 4 3 5 3 4 4 3
178 5 5 4 4 3 4 4 3 3 3 3 5 5 4 3 5 3 4 4 4 5
179 4 4 4 4 5 4 2 2 2 4 4 4 3 3 3 5 5 5 5 4 4
180 4 5 4 5 5 5 5 5 4 4 5 5 4 3 3 4 3 3 3 5 3
181 5 5 4 5 5 4 5 4 4 5 5 5 4 5 5 5 5 5 5 5 5
182 3 3 5 5 5 3 3 3 3 5 5 5 5 3 3 5 5 5 4 4 5
183 5 4 5 5 5 4 5 5 5 5 5 4 5 5 5 5 4 5 2 4 5
184 4 4 4 4 4 4 4 4 4 4 4 5 5 5 4 5 4 4 5 4 5
185 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
186 4 4 2 4 2 4 3 3 3 4 4 2 3 3 3 3 3 3 4 4 4
187 5 5 5 5 5 5 3 3 3 4 4 5 5 5 5 5 5 5 5 3 4
188 5 5 4 3 5 3 2 1 5 5 4 3 5 1 2 5 3 2 4 5 2
189 5 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
190 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
191 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
192 5 5 5 5 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 4
193 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
194 5 5 5 5 5 5 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
195 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
196 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
197 5 5 5 5 5 5 5 5 5 4 4 4 4 5 5 5 5 5 5 5 5
198 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
199 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
200 4 4 4 4 4 4 2 2 2 4 2 4 2 2 2 4 2 2 4 2 4
201 4 4 4 4 4 4 4 2 2 4 4 4 4 4 4 4 4 2 4 4 4
202 4 4 4 4 4 4 2 2 2 4 2 4 2 2 2 2 4 4 4 4 4
203 4 5 5 5 5 4 3 3 2 5 5 5 4 3 2 2 3 2 4 4 3
204 5 5 5 5 4 5 3 3 2 5 5 2 2 5 4 1 4 1 5 4 5
205 5 5 4 5 5 5 3 3 3 5 5 5 3 3 3 5 5 2 2 3 4
206 5 5 5 5 5 5 4 3 4 4 4 4 2 2 2 2 4 2 4 5 5
207 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 2 4 4 4 4 4
208 4 5 5 5 5 4 3 3 3 4 4 4 4 4 4 4 3 3 4 4 4
209 3 3 3 3 3 3 1 2 2 2 2 3 2 2 2 2 1 2 2 3 2
210 4 4 4 4 2 4 4 4 2 2 2 4 4 4 4 4 4 4 4 4 4
211 4 5 5 4 5 4 4 4 4 4 4 5 5 5 4 4 4 4 5 4 4
212 5 5 4 4 4 4 2 2 2 2 2 2 4 4 5 4 4 5 5 2 5
213 4 4 4 4 3 3 3 3 3 3 4 3 2 2 2 2 2 3 3 3 3
214 4 4 4 3 3 3 4 2 4 3 3 2 2 1 4 4 1 1 4 4 3
215 4 4 4 4 4 4 2 2 2 4 4 4 2 2 2 2 4 2 4 4 4
216 4 3 4 3 4 4 3 3 3 3 4 4 2 2 2 2 4 2 4 4 4
217 5 5 5 3 4 4 5 5 3 5 5 4 2 2 2 4 2 2 4 4 4
218 5 4 5 5 5 3 3 3 3 5 5 3 3 3 3 3 3 4 3 4 4
219 4 4 3 4 3 3 3 3 3 4 2 3 4 4 4 3 2 4 4 3 5
220 4 4 4 4 4 4 4 4 4 4 4 4 4 1 1 1 1 1 1 1 1
221 4 4 5 5 5 4 4 4 4 3 3 3 2 2 2 2 5 2 1 4 4
222 4 4 4 3 3 3 3 3 3 2 2 2 2 2 2 2 3 2 2 4 3
223 4 4 4 4 4 3 2 3 3 3 3 3 2 2 2 2 2 2 4 4 4
224 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 4 3
225 5 5 5 4 5 4 3 4 4 4 3 1 1 1 1 1 5 1 5 5 5
226 4 3 4 4 4 3 3 3 3 5 5 3 4 3 1 5 1 1 3 4 4
227 5 4 5 5 4 4 4 4 3 4 4 3 1 3 4 4 3 4 3 5 5
228 5 5 5 5 5 4 4 5 5 4 4 4 3 3 5 5 3 3 3 2 3
229 4 3 4 4 2 4 3 3 3 2 4 4 2 2 2 4 3 2 3 4 2
230 4 4 4 4 4 4 4 2 3 4 4 4 2 2 2 2 3 2 2 3 3
231 4 4 4 4 4 4 3 3 2 4 4 5 4 4 4 4 4 4 3 4 4
232 4 4 4 4 4 4 4 4 4 4 4 4 4 2 2 2 4 4 4 4 4
233 4 4 4 4 4 4 2 4 3 3 3 4 4 4 4 4 2 4 4 2 4
234 4 4 4 2 4 5 4 4 4 5 4 5 2 2 2 5 5 2 4 2 2
235 4 4 5 3 4 4 4 3 4 4 4 5 3 2 4 4 4 4 3 5 4
236 4 4 4 4 4 3 3 3 4 4 4 4 2 2 2 2 4 2 2 4 4
237 4 4 4 4 4 4 2 4 2 4 4 4 2 5 4 2 2 4 4 4 4
238 4 4 4 4 4 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
239 4 5 3 4 4 3 3 2 2 4 4 4 5 4 3 3 3 4 4 3 4
240 3 2 1 3 2 1 2 1 2 1 2 2 2 1 3 2 2 1 2 1 2
241 4 4 3 4 3 3 2 2 2 2 2 3 1 1 1 1 3 1 2 3 1
242 4 4 4 4 4 3 4 3 4 4 4 4 3 3 2 2 4 2 4 4 2
243 4 4 4 4 4 5 2 2 4 4 4 2 2 2 2 2 5 2 4 4 4
244 4 3 3 4 4 4 3 4 3 5 3 4 4 4 4 3 3 3 2 4 3
245 3 4 4 4 5 4 4 4 3 3 4 4 4 4 3 4 4 3 4 3 4
246 3 4 5 4 4 3 3 3 4 3 4 4 2 2 2 2 5 2 4 4 1
247 5 5 5 5 4 4 4 4 4 4 5 4 3 3 4 5 4 3 4 4 4
248 4 4 4 4 4 4 3 3 3 4 4 4 4 4 4 4 4 4 4 4 5
249 4 5 5 5 5 4 4 5 5 5 5 5 5 5 4 4 4 3 4 4 4
250 4 2 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 4 4
251 4 5 5 4 5 5 5 4 3 4 4 3 4 4 4 4 3 4 3 4 3
252 4 5 5 5 5 4 4 4 4 4 4 4 3 3 3 4 4 3 3 4 2
253 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 5 5 5 5 5 5
254 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
255 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
256 4 5 3 4 4 3 4 5 4 4 4 5 4 4 4 3 4 4 4 4 3
257 5 4 5 5 5 4 3 5 5 4 3 4 4 3 5 5 5 4 3 4 4
258 3 3 3 3 5 5 4 4 3 4 5 5 4 3 3 5 5 4 3 4 4
259 4 4 4 4 4 4 3 3 3 3 4 5 5 5 5 4 4 5 5 5 2
260 4 4 3 4 5 4 4 5 5 4 4 3 3 4 5 3 4 4 3 5 4
261 4 4 5 5 3 3 4 4 4 3 3 4 4 4 5 5 5 4 4 4 3
262 5 4 3 5 4 3 4 4 3 3 5 5 4 3 4 4 3 3 4 4 5
263 5 4 4 5 5 4 4 4 4 4 4 5 1 1 3 5 4 4 3 4 5
264 3 4 3 2 2 3 3 3 3 4 4 2 2 2 2 2 3 2 2 2 2
265 5 5 4 5 5 4 3 3 3 5 5 5 5 5 4 4 4 4 4 5 5
266 5 5 4 4 4 4 4 4 4 4 4 4 3 4 3 3 3 4 4 4 5
267 5 5 5 5 5 5 4 4 4 4 5 5 2 2 2 4 5 2 4 3 3
268 5 5 5 4 5 5 5 3 5 4 5 5 2 2 2 2 3 2 3 3 3
269 4 5 4 5 5 5 5 5 5 5 5 1 1 1 5 5 1 1 5 4 5
270 3 4 4 4 4 4 3 1 1 1 4 4 1 1 1 1 1 4 1 4 4
271 4 4 5 2 4 2 4 2 2 4 5 4 2 2 2 4 2 2 2 4 2
272 3 2 2 3 2 3 2 3 3 4 5 2 1 1 1 5 5 1 3 5 4
273 4 1 4 4 4 4 4 4 1 4 4 4 4 4 4 4 4 4 4 4 4
274 4 3 4 4 4 4 3 3 3 2 4 4 2 2 2 4 3 2 3 3 3
275 5 5 5 5 1 4 4 4 4 4 4 4 1 5 1 5 2 1 1 3 2
276 5 2 2 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
277 4 4 5 5 5 4 4 5 5 5 5 5 2 2 2 2 5 2 3 2 5
278 4 4 3 4 4 4 3 3 3 4 5 4 3 2 3 3 4 2 3 4 4
279 5 4 5 5 4 4 4 4 4 5 5 5 2 2 2 4 4 2 4 4 4
280 5 5 5 5 5 4 4 4 4 5 5 4 2 2 2 2 4 2 4 4 3
281 4 4 2 4 4 4 2 4 3 3 4 4 2 2 2 2 4 2 1 2 1
282 4 3 4 4 4 4 3 4 3 4 4 4 2 2 2 2 4 2 2 4 4
283 5 5 4 4 4 4 2 1 4 3 3 3 2 2 2 5 4 1 1 3 3
284 4 4 4 4 4 4 3 3 4 4 4 3 2 2 4 5 5 5 4 4 4
285 4 4 4 4 4 4 4 4 3 4 4 4 2 4 2 2 2 2 4 4 4
286 4 4 4 4 4 4 4 3 4 3 4 2 4 2 2 4 2 4 4 3 2
287 4 4 4 4 3 4 4 4 4 4 4 4 2 2 2 2 2 2 4 4 2
288 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
289 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
290 4 4 4 4 4 4 4 4 4 4 4 4 2 2 4 4 4 4 4 4 4
291 4 2 4 4 4 4 4 4 4 4 4 4 4 2 4 4 4 4 4 4 4
292 4 4 3 2 1 3 4 3 4 1 2 4 4 4 3 4 4 4 4 4 3
293 5 4 5 5 5 4 4 5 4 5 5 5 5 2 2 2 4 2 4 4 4
294 5 5 5 5 5 5 4 4 4 5 5 5 4 1 1 5 5 1 4 5 5
295 4 2 4 3 4 3 2 3 2 4 4 1 3 4 2 4 4 4 3 4 3
296 4 5 4 5 5 4 3 2 2 5 5 5 5 5 4 5 4 5 4 4 5
297 4 4 2 4 4 2 2 2 2 5 5 5 2 2 2 2 4 2 4 3 3
298 5 4 3 2 2 3 4 4 3 3 2 1 3 4 5 5 4 4 3 3 3
299 5 5 5 5 4 4 3 3 3 3 4 4 2 2 2 3 3 2 4 4 4
300 4 4 4 4 4 4 3 3 3 4 4 4 2 2 2 2 4 2 4 4 4
301 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
302 4 4 4 4 4 4 3 4 2 4 4 4 3 3 3 3 4 4 4 4 4
303 5 3 4 4 4 3 5 4 3 5 5 4 4 4 5 4 4 4 5 3 4
304 4 4 4 2 3 5 2 3 4 5 4 4 5 5 5 5 5 2 4 4 5
305 5 2 2 2 5 5 2 5 4 4 4 4 5 5 5 5 5 5 5 5 5
306 3 2 3 3 3 2 2 2 2 3 4 2 2 2 2 2 2 1 1 2 1
307 3 3 3 2 2 1 3 3 2 3 3 3 1 1 1 1 3 1 1 3 2
308 4 4 4 4 5 3 3 4 3 4 4 4 2 2 2 4 2 2 4 4 4
309 4 3 4 2 2 4 4 3 3 4 4 4 2 3 3 4 4 2 2 2 1
310 4 4 4 4 2 4 2 4 2 4 2 4 2 4 4 2 4 2 2 4 4
311 5 5 4 4 4 4 4 4 4 2 2 4 3 3 2 4 4 2 3 5 2
312 5 4 5 5 4 5 4 4 4 4 2 4 5 5 4 2 5 5 4 4 2
313 4 4 4 4 4 4 2 2 2 2 4 4 2 2 2 4 4 2 4 4 4
314 5 5 5 5 5 5 2 4 4 4 4 2 2 2 2 2 4 2 2 4 2
315 5 5 5 5 5 5 4 4 1 5 5 5 5 4 4 5 5 5 5 4 4
316 5 4 4 4 4 4 2 2 4 4 4 4 2 2 2 2 4 2 2 4 3
317 4 5 3 4 4 4 4 4 2 4 4 5 2 2 2 4 4 2 2 4 4
318 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
319 5 5 5 5 5 5 5 5 4 3 4 5 1 1 1 5 4 1 3 3 5
320 3 3 3 5 5 5 3 3 3 3 5 4 1 1 1 5 5 1 3 3 3
321 5 4 5 5 5 4 4 5 4 4 4 4 4 4 4 5 5 5 4 5 5
322 2 5 4 4 4 3 2 2 2 5 5 4 1 1 1 1 5 1 5 5 4
323 5 5 5 5 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5
324 4 4 4 4 4 3 2 3 2 2 3 4 4 4 2 4 4 2 4 2 2
325 4 4 4 4 4 3 2 2 2 2 4 4 2 2 2 4 4 2 2 4 4
326 5 5 5 5 5 2 2 2 2 5 2 5 2 2 5 5 5 5 2 2 5
327 4 4 4 5 5 5 4 4 4 4 4 5 5 5 5 5 4 4 5 5 5
328 5 5 4 4 5 4 4 2 2 4 4 4 4 4 4 4 3 4 5 4 4
329 5 2 2 3 3 5 3 3 3 4 3 3 2 4 4 4 4 5 5 5 5
330 4 4 5 5 4 4 3 3 3 5 5 5 3 3 4 4 4 4 4 4 4
331 4 5 4 5 5 3 2 2 5 5 5 5 2 5 5 5 5 3 3 4 4
332 3 5 5 5 5 3 3 3 2 4 5 4 1 1 1 1 5 1 1 5 3
333 5 3 5 5 5 3 3 2 2 3 3 5 1 1 1 1 3 1 1 1 5
334 5 5 3 2 2 2 5 5 2 2 5 5 5 5 2 2 2 2 5 5 5
335 5 3 3 5 4 3 3 4 4 4 4 4 2 2 2 5 2 2 2 2 2
336 4 3 4 4 2 1 3 4 5 2 5 1 4 5 3 4 3 5 1 2 1
337 5 5 5 5 4 5 3 3 2 5 3 5 5 5 4 5 5 5 4 3 5
338 5 5 5 5 3 3 4 3 4 4 3 3 3 3 3 4 4 4 4 4 4
339 4 3 3 4 5 3 2 2 2 4 4 4 1 1 1 1 5 1 1 3 4
340 4 3 3 3 5 3 1 4 1 1 5 1 4 2 1 5 5 5 5 5 1
341 5 2 5 5 5 2 2 2 2 4 4 3 2 2 2 3 2 2 2 5 1
342 5 2 5 3 5 5 2 2 2 5 5 5 4 4 4 4 4 4 5 5 5
343 5 5 4 4 4 3 2 2 2 4 3 3 2 4 2 4 2 3 4 4 4
344 5 5 3 5 5 3 3 3 3 5 3 5 3 3 3 3 3 3 3 5 3
345 5 5 5 2 2 4 2 2 3 4 2 2 2 2 3 3 3 3 3 3 3
346 5 4 4 4 4 3 3 2 2 4 4 4 4 4 4 3 4 4 3 3 4
347 3 4 3 4 3 5 2 2 2 4 4 4 2 2 2 2 3 2 2 4 3
348 5 4 2 4 3 3 2 2 4 4 4 4 2 2 2 2 4 2 4 3 4
349 4 5 4 3 4 3 4 3 3 5 4 5 1 1 1 1 1 1 1 5 4
350 4 5 3 4 4 4 1 1 1 4 5 5 1 1 1 1 4 1 4 4 4
351 5 5 5 5 5 4 1 1 1 5 5 4 1 1 1 1 1 1 5 5 5
352 5 5 5 5 4 4 2 2 1 3 5 5 4 4 4 4 4 4 4 5 5
353 5 4 4 4 4 4 4 4 3 3 5 5 3 2 3 4 5 3 3 2 3
354 4 4 3 4 4 3 2 2 2 4 5 4 2 2 2 2 4 2 2 5 3
355 5 5 4 4 3 4 2 2 2 3 4 4 2 2 2 5 4 2 2 5 4
356 3 3 3 4 4 4 2 2 1 3 5 4 1 1 1 1 3 1 1 5 4
357 5 5 4 5 4 5 2 2 2 3 5 4 5 5 5 4 4 5 5 4 5
358 5 5 4 4 4 4 2 2 2 2 4 4 2 2 2 4 4 2 2 4 3
359 5 5 5 4 4 4 2 2 2 2 4 4 4 4 5 5 4 5 5 4 5
360 4 4 4 4 4 4 2 2 2 2 4 4 2 2 2 4 4 2 2 4 4
361 4 4 5 5 4 5 2 2 2 2 5 2 5 5 5 5 5 5 5 4 5
362 4 4 5 5 5 3 3 3 3 4 4 4 3 4 3 4 4 3 3 3 3
363 4 4 3 3 4 4 1 1 1 4 5 4 2 2 2 2 2 2 2 4 4
364 4 4 4 5 5 5 5 4 3 4 4 4 3 2 1 4 4 3 2 3 4
365 4 4 4 4 4 4 1 1 1 4 5 4 1 1 1 1 3 1 1 4 4
366 5 5 2 2 5 5 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
367 4 4 4 4 2 3 3 2 2 4 4 4 4 4 4 2 4 4 2 3 2
368 4 4 4 4 4 4 4 2 2 2 2 4 4 4 4 2 4 4 4 2 2
369 3 3 3 3 3 3 2 2 5 5 5 5 1 1 1 1 5 2 3 3 5
370 3 3 3 3 3 3 3 2 3 5 5 4 1 1 1 1 4 1 3 3 4
371 3 2 3 3 3 3 3 2 3 5 5 3 1 3 1 1 1 1 3 5 5
372 4 3 3 4 4 4 2 2 2 4 4 4 2 2 2 2 2 4 4 4 4
373 5 4 5 5 3 5 4 4 3 5 5 5 2 2 1 1 4 1 5 1 5
374 5 5 5 5 5 4 1 1 1 4 4 4 1 1 1 1 4 1 1 4 4
375 4 3 2 4 1 3 5 5 4 3 2 1 5 3 2 1 2 3 1 3 2
376 4 4 4 4 4 4 3 3 3 4 4 4 3 3 4 5 5 5 4 4 4
377 4 5 5 5 5 3 3 3 3 4 4 4 5 4 4 5 4 4 3 3 5
378 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
379 4 4 4 4 4 4 2 2 2 2 4 4 2 2 2 4 4 2 2 4 4
380 3 4 4 4 3 4 4 4 2 4 4 4 4 4 4 4 4 4 4 4 2
381 4 4 4 4 4 4 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4
382 1 4 4 2 5 5 2 2 3 4 5 4 1 1 1 1 3 1 1 4 4
383 5 4 4 2 5 5 2 2 2 5 5 3 5 5 5 5 5 5 4 5 5
384 4 4 4 4 4 4 2 2 2 4 4 4 2 2 2 2 2 2 2 2 4
385 4 4 4 4 4 4 3 1 1 3 4 3 1 1 1 4 1 1 1 5 4
386 5 5 5 5 5 2 2 2 2 3 3 4 1 1 1 1 1 1 1 1 1
387 2 2 1 1 1 1 1 1 1 1 1 4 1 1 1 5 5 1 1 5 5
388 4 3 3 3 4 4 1 1 1 4 5 4 1 1 1 4 4 1 1 4 4
389 5 5 4 4 4 4 1 1 1 5 5 5 1 1 1 4 4 1 1 5 5
390 4 4 4 4 4 5 1 1 1 5 5 5 4 4 4 4 4 4 4 4 5
391 5 5 4 4 4 4 1 1 1 3 5 4 1 1 1 1 3 1 1 5 5
392 4 4 4 4 4 4 1 1 1 4 4 4 1 1 1 1 4 1 1 4 4
393 5 5 5 5 4 4 1 1 1 5 5 4 1 1 1 4 4 1 1 4 4
394 5 5 4 4 4 5 1 1 1 5 5 5 1 1 1 4 4 1 1 4 4
395 4 5 5 5 5 4 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4
396 5 5 4 3 4 4 4 4 4 4 4 3 5 5 5 5 3 4 4 4 5
397 5 5 4 4 4 4 5 4 4 4 4 4 4 3 3 2 4 4 3 4 4
398 4 4 5 4 4 5 4 4 3 4 4 4 5 5 4 3 3 4 3 4 5
399 3 3 3 4 4 4 3 3 3 3 4 4 4 4 4 4 4 4 4 4 3
400 4 4 4 4 4 1 1 1 1 1 1 4 1 1 1 4 4 1 1 4 4
401 4 4 4 4 4 4 1 1 1 4 4 4 4 4 4 4 4 5 5 1 5
402 5 1 4 4 4 1 1 1 3 3 3 4 4 4 5 5 2 4 3 1 4
403 5 5 5 5 5 3 2 1 1 4 4 4 1 1 1 1 3 2 2 4 3
404 5 3 3 3 5 5 2 2 1 4 2 4 4 4 4 1 4 4 2 2 5
405 5 5 5 5 4 1 1 1 1 4 4 1 1 1 1 1 4 2 2 5 4
406 5 5 5 5 5 1 1 1 1 3 3 4 2 2 1 4 4 1 1 4 4
407 3 3 4 4 3 1 1 1 1 4 4 3 1 1 1 1 1 1 1 3 1
408 5 5 5 5 5 1 1 1 1 4 4 4 1 1 1 5 5 2 2 4 4
409 5 5 5 5 5 5 1 1 1 4 4 4 1 1 1 4 4 1 1 5 4
410 5 5 5 5 5 1 1 1 1 4 3 4 1 1 1 4 3 1 1 3 5
411 5 5 5 5 5 5 1 5 1 5 3 4 4 4 4 4 4 4 1 3 5
412 5 5 5 5 5 1 1 1 1 3 4 4 3 3 3 3 3 4 1 4 5
413 4 5 4 5 5 3 3 4 2 4 4 5 3 4 4 5 4 3 5 5 4
414 4 3 4 4 5 4 4 4 4 5 4 5 4 4 3 5 4 4 4 5 4
415 5 4 3 4 3 3 2 1 1 3 3 5 4 2 2 5 4 4 5 5 2
416 5 4 5 4 4 3 4 3 3 4 5 4 4 3 3 4 3 4 3 4 4
417 5 5 1 3 5 4 1 1 1 3 3 4 3 4 1 1 1 3 3 3 3
418 3 5 5 5 4 5 3 3 4 2 2 5 3 3 3 5 5 3 5 5 3
419 5 5 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3
420 4 4 4 5 4 4 4 3 2 4 4 4 4 5 5 5 5 5 4 4 4
421 1 2 3 4 3 4 3 3 2 2 2 3 5 5 5 5 5 5 4 3 5
422 4 5 4 5 5 4 4 4 4 5 4 5 4 4 4 5 5 5 5 3 4
423 5 3 4 3 3 4 4 3 4 5 4 3 5 5 5 5 4 4 4 3 5
424 5 5 3 5 5 5 5 2 3 4 4 5 5 5 5 3 3 4 4 4 5
425 5 5 5 5 5 5 5 5 5 4 4 5 5 5 5 5 4 5 5 1 5
426 4 3 5 5 5 5 3 3 5 2 3 4 3 3 3 4 3 3 3 3 4
427 1 5 5 5 5 4 2 2 2 5 3 5 4 4 4 5 4 4 3 5 1
428 5 4 4 4 4 4 3 3 3 4 4 4 3 3 3 3 3 3 3 4 3
429 5 5 4 4 3 3 2 2 4 4 4 4 3 3 4 4 3 4 4 4 5
430 5 5 4 4 3 3 2 2 4 5 3 3 4 3 4 4 2 4 4 2 4
431 4 4 2 4 4 4 4 2 5 5 5 4 2 2 2 2 2 2 2 4 4
432 5 5 4 5 5 4 5 5 3 5 3 5 3 3 3 4 4 3 3 5 5
433 5 5 4 5 5 4 4 4 4 5 4 5 5 5 5 5 5 5 5 4 4
434 1 2 2 2 3 4 1 1 2 4 2 4 1 1 1 1 1 1 1 5 1
435 4 3 3 2 4 4 2 1 2 1 1 2 3 2 4 4 3 2 3 4 2
436 4 3 3 4 3 2 2 2 4 1 1 4 1 1 4 4 4 4 2 4 2
437 5 4 4 5 5 5 2 2 4 4 3 4 5 5 5 5 5 5 4 4 5
438 4 3 4 2 2 4 3 3 4 4 1 2 1 1 1 4 3 1 4 4 1
439 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3 4 5 3 5 5
440 5 5 5 5 5 5 5 5 4 5 4 5 5 5 5 5 5 5 5 5 5
441 4 4 4 4 4 4 3 3 3 4 4 3 3 3 3 3 4 4 4 4 4
442 5 5 5 3 3 3 3 3 3 3 3 3 3 3 3 4 3 3 3 5 3
443 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
444 5 5 5 5 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
445 5 5 5 5 4 4 4 3 3 4 5 5 2 2 2 2 3 1 1 4 4
446 4 5 4 4 4 4 2 2 2 4 4 4 1 1 1 4 4 3 4 3 4
447 5 4 4 4 4 5 4 4 2 4 4 4 3 3 3 4 4 4 3 4 4
448 4 4 4 4 5 4 3 3 3 4 4 4 5 2 3 5 5 3 4 3 4
449 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
450 4 4 4 4 4 4 4 4 4 4 4 4 1 1 1 1 4 1 1 4 4
451 5 5 4 4 5 5 3 3 3 3 3 3 3 3 3 5 2 2 4 4 4
452 5 5 4 4 5 4 5 5 2 3 4 5 5 5 5 5 5 5 5 5 5
453 5 5 4 5 5 4 4 4 4 4 5 4 4 4 4 4 4 4 4 4 4
454 4 4 3 4 4 4 4 4 2 3 4 4 4 4 4 4 4 4 4 4 4
455 4 3 1 1 5 5 5 5 1 5 5 4 4 4 4 5 1 4 4 5 4
456 5 5 5 5 5 4 3 4 3 3 3 3 3 3 2 3 2 2 5 5 3
457 5 5 5 5 4 3 3 3 3 5 5 4 4 3 3 5 4 3 4 4 4
458 5 5 5 5 4 3 3 3 3 3 3 4 3 3 3 4 3 4 4 5 4
459 4 4 4 4 4 3 2 2 2 4 4 3 2 2 2 2 3 2 2 3 3
460 5 4 4 4 4 4 2 2 2 3 3 5 4 4 4 5 4 4 4 4 4
461 4 4 3 3 3 3 3 3 3 4 4 4 4 3 3 3 3 3 3 4 3
462 5 5 5 5 5 5 5 5 2 5 5 5 2 2 2 2 5 2 2 5 5
463 5 5 4 4 4 4 5 4 4 4 4 5 4 5 5 5 5 4 4 4 5
464 5 5 5 5 5 5 3 3 3 5 5 4 4 5 5 4 4 4 5 5 4
465 5 5 5 5 5 5 5 5 5 5 5 5 1 1 1 5 5 1 1 5 5
466 5 5 4 4 5 5 3 3 3 3 3 5 5 5 5 5 5 5 5 5 5
467 5 4 4 3 4 4 3 3 3 4 4 4 2 2 4 3 3 3 4 3 3
468 5 5 5 5 5 5 4 4 3 3 4 5 5 5 5 5 3 3 5 5 5
469 5 5 4 4 4 3 3 3 3 4 3 4 3 3 3 3 3 3 2 3 4
470 4 4 4 4 4 3 3 3 3 3 3 5 5 5 5 5 4 4 4 5 5
471 4 5 3 4 3 5 2 3 3 3 3 4 2 2 2 4 4 2 2 4 3
472 5 3 3 3 4 4 3 3 4 4 2 2 2 2 2 2 4 3 3 3 3
473 4 3 4 4 5 4 4 2 2 4 4 4 4 4 2 2 3 3 3 2 4
474 5 5 5 5 5 5 5 5 5 5 4 5 5 5 5 3 5 5 5 5 5
475 5 4 3 5 4 4 4 1 1 4 4 5 5 5 4 4 3 4 3 2 3
476 5 5 4 3 4 4 4 4 4 4 4 4 1 1 4 4 4 4 2 2 2
477 4 5 3 4 4 5 5 2 3 4 4 4 5 4 5 3 4 4 5 4 5
478 5 2 5 2 5 2 5 2 5 5 5 2 2 2 2 2 2 2 2 5 5
479 4 5 4 4 5 4 3 3 3 5 5 4 1 1 1 4 3 1 4 4 4
480 4 5 3 3 3 2 2 2 2 3 5 3 4 3 3 3 3 3 2 3 3
481 5 5 5 5 5 5 5 4 4 5 5 5 2 2 2 5 4 2 2 4 4
482 4 4 4 5 5 5 5 5 4 4 4 5 5 1 1 5 4 1 4 5 5
483 4 4 3 3 4 3 2 2 2 3 5 3 3 2 2 3 2 3 2 3 3
484 4 5 4 4 4 4 3 3 3 4 4 4 3 3 3 4 3 3 3 4 4
485 4 5 4 4 4 3 3 3 3 3 5 5 3 3 2 4 3 4 3 4 4
486 4 5 3 5 4 5 5 3 4 4 3 3 5 4 3 5 3 3 2 3 3
487 4 5 4 4 4 3 3 3 3 4 5 5 4 3 2 4 3 3 2 3 4
488 5 3 4 5 4 5 3 4 4 5 5 5 1 1 5 5 1 1 3 4 5
489 5 4 4 4 4 3 3 3 4 4 4 4 4 3 4 4 4 4 4 4 4
490 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
491 5 4 3 3 5 3 2 2 3 4 5 3 3 3 4 3 2 3 3 3 3
492 4 4 4 5 5 5 4 4 4 4 4 4 5 5 4 4 4 4 5 4 4
493 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5
494 5 5 5 5 5 5 4 4 4 5 5 5 4 4 4 5 5 5 4 4 5
495 5 5 5 5 5 5 5 5 4 4 5 5 2 1 1 5 1 1 1 4 3
496 4 4 4 4 4 3 3 3 3 3 3 3 3 4 2 4 3 4 3 4 3
497 5 4 4 4 4 3 4 3 4 3 4 3 4 3 2 4 3 3 4 3 3
498 4 4 4 3 3 3 3 3 3 4 5 4 4 3 3 4 3 3 3 2 4
499 4 5 3 5 4 2 1 2 2 3 3 4 2 2 2 2 4 1 3 5 3
500 5 5 4 4 4 3 3 3 3 3 3 5 2 1 5 4 4 1 1 4 3
501 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 4 3 4 4 4
502 4 3 4 5 4 5 4 4 4 4 3 4 2 2 4 4 3 3 3 3 4
503 5 4 4 5 2 4 3 5 2 1 4 5 4 2 4 1 5 5 5 4 3
504 5 5 5 5 5 5 5 5 4 5 5 5 5 4 5 5 5 5 5 3 5
505 5 4 3 3 3 4 3 4 1 3 4 3 5 3 4 5 4 5 5 5 4
506 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 5 5 4 3
507 5 5 5 5 5 5 5 5 4 5 4 5 5 3 5 5 5 5 5 5 5
508 3 5 4 4 4 3 3 3 3 4 4 4 4 3 3 3 3 3 4 4 3
509 4 4 4 4 4 2 1 1 1 3 3 4 4 4 4 4 4 3 4 4 4
510 5 5 5 5 5 1 4 4 4 4 4 4 4 4 4 4 4 4 4 1 4
511 5 5 3 4 3 3 3 3 5 3 3 2 2 2 2 3 3 2 2 4 3
512 4 4 4 4 4 4 4 4 4 4 4 3 3 4 3 4 4 3 3 3 3
513 4 5 4 5 5 3 3 2 2 3 3 5 4 4 4 3 3 3 3 4 3
514 5 5 5 5 5 5 1 1 1 1 1 5 5 5 5 1 1 5 5 3 4
515 4 3 3 3 3 2 3 3 3 3 4 4 3 3 3 3 3 3 5 4 4
516 5 5 5 5 5 5 5 3 4 3 3 5 5 5 5 3 5 5 5 5 5
517 5 5 5 4 5 5 4 4 4 4 4 5 5 5 4 4 4 4 4 4 5
518 4 4 4 4 4 4 3 3 3 3 4 3 2 2 2 5 5 2 3 4 4
519 5 4 4 4 4 4 4 4 4 4 4 2 2 2 2 5 5 2 3 4 4
520 4 3 4 4 4 3 3 3 3 4 4 4 2 2 2 2 4 2 3 3 4
521 4 4 4 4 4 3 3 3 3 4 4 3 2 2 2 4 4 2 3 4 4
522 4 4 4 4 4 4 3 3 3 4 4 3 2 2 2 5 5 2 3 4 5
523 5 4 4 4 4 3 5 3 3 3 5 4 2 2 2 5 5 2 3 4 4
524 4 4 4 4 4 4 3 3 3 3 3 4 2 2 2 2 4 1 3 4 4
525 4 4 4 4 4 4 4 4 3 4 4 3 2 2 2 3 3 3 3 3 3
526 5 5 5 5 5 4 4 4 4 5 5 4 4 4 4 5 5 4 4 4 5
In [96]:
data_HS_Factor.corr().style.applymap(highlight_cell)
Out[96]:
Feature Brand Battery back up Keypad Display Sound Size of the screen Electronic media Print media Hoardings Friends Family members Voice clarity MP3 facility Bluetooth Camera pixels FM radio Games Video Memory size Price Stylish
Feature                                          
Brand 1.000000 0.447500 0.368049 0.352448 0.299773 0.219962 0.241112 0.238421 0.202074 0.220499 0.205741 0.189865 0.208792 0.203259 0.226424 0.251729 0.181322 0.219793 0.208544 0.111346 0.285657
Battery back up 0.447500 1.000000 0.455116 0.473122 0.356497 0.275212 0.183919 0.156460 0.128429 0.219719 0.188218 0.313370 0.188104 0.177044 0.175327 0.187393 0.174319 0.151544 0.194127 0.207753 0.226921
Keypad 0.368049 0.455116 1.000000 0.550146 0.463320 0.273884 0.287988 0.297248 0.266476 0.291228 0.211269 0.291135 0.140184 0.163924 0.176070 0.226731 0.243890 0.166411 0.173915 0.153351 0.279279
Display 0.352448 0.473122 0.550146 1.000000 0.515270 0.338573 0.266246 0.291500 0.196218 0.251557 0.241758 0.409060 0.232849 0.224646 0.216971 0.231296 0.245455 0.226117 0.188534 0.153898 0.311172
Sound 0.299773 0.356497 0.463320 0.515270 1.000000 0.411655 0.238118 0.249039 0.177031 0.324270 0.315028 0.401818 0.216129 0.198162 0.210070 0.232918 0.259878 0.212409 0.219263 0.240665 0.359297
Size of the screen 0.219962 0.275212 0.273884 0.338573 0.411655 1.000000 0.378187 0.410941 0.264674 0.290560 0.239298 0.345437 0.305405 0.290902 0.322440 0.288611 0.269994 0.278295 0.346447 0.264013 0.380629
Electronic media 0.241112 0.183919 0.287988 0.266246 0.238118 0.378187 1.000000 0.754414 0.636217 0.293009 0.240326 0.252094 0.373971 0.344800 0.343241 0.319687 0.209542 0.341613 0.323455 0.175582 0.209705
Print media 0.238421 0.156460 0.297248 0.291500 0.249039 0.410941 0.754414 1.000000 0.651765 0.288497 0.240645 0.240040 0.376907 0.353263 0.370847 0.351341 0.284418 0.349543 0.353232 0.182963 0.220291
Hoardings 0.202074 0.128429 0.266476 0.196218 0.177031 0.264674 0.636217 0.651765 1.000000 0.331637 0.177769 0.108796 0.266229 0.247542 0.318332 0.303566 0.236877 0.260842 0.296166 0.117033 0.148987
Friends 0.220499 0.219719 0.291228 0.251557 0.324270 0.290560 0.293009 0.288497 0.331637 1.000000 0.524864 0.303580 0.151109 0.164342 0.159330 0.179428 0.213524 0.151927 0.193085 0.167739 0.330806
Family members 0.205741 0.188218 0.211269 0.241758 0.315028 0.239298 0.240326 0.240645 0.177769 0.524864 1.000000 0.305155 0.141054 0.102727 0.067721 0.154411 0.213426 0.093876 0.161557 0.196582 0.271532
Voice clarity 0.189865 0.313370 0.291135 0.409060 0.401818 0.345437 0.252094 0.240040 0.108796 0.303580 0.305155 1.000000 0.333644 0.282737 0.263019 0.294226 0.320801 0.231258 0.249381 0.243936 0.367314
MP3 facility 0.208792 0.188104 0.140184 0.232849 0.216129 0.305405 0.373971 0.376907 0.266229 0.151109 0.141054 0.333644 1.000000 0.802965 0.730100 0.475656 0.310699 0.720118 0.616411 0.167273 0.338187
Bluetooth 0.203259 0.177044 0.163924 0.224646 0.198162 0.290902 0.344800 0.353263 0.247542 0.164342 0.102727 0.282737 0.802965 1.000000 0.794365 0.445135 0.310691 0.732494 0.585997 0.182164 0.373247
Camera pixels 0.226424 0.175327 0.176070 0.216971 0.210070 0.322440 0.343241 0.370847 0.318332 0.159330 0.067721 0.263019 0.730100 0.794365 1.000000 0.525500 0.347600 0.787034 0.619238 0.192735 0.381609
FM radio 0.251729 0.187393 0.226731 0.231296 0.232918 0.288611 0.319687 0.351341 0.303566 0.179428 0.154411 0.294226 0.475656 0.445135 0.525500 1.000000 0.408284 0.484568 0.424790 0.234218 0.364601
Games 0.181322 0.174319 0.243890 0.245455 0.259878 0.269994 0.209542 0.284418 0.236877 0.213524 0.213426 0.320801 0.310699 0.310691 0.347600 0.408284 1.000000 0.378922 0.346574 0.237919 0.329868
Video 0.219793 0.151544 0.166411 0.226117 0.212409 0.278295 0.341613 0.349543 0.260842 0.151927 0.093876 0.231258 0.720118 0.732494 0.787034 0.484568 0.378922 1.000000 0.633084 0.183343 0.348966
Memory size 0.208544 0.194127 0.173915 0.188534 0.219263 0.346447 0.323455 0.353232 0.296166 0.193085 0.161557 0.249381 0.616411 0.585997 0.619238 0.424790 0.346574 0.633084 1.000000 0.279794 0.407858
Price 0.111346 0.207753 0.153351 0.153898 0.240665 0.264013 0.175582 0.182963 0.117033 0.167739 0.196582 0.243936 0.167273 0.182164 0.192735 0.234218 0.237919 0.183343 0.279794 1.000000 0.380925
Stylish 0.285657 0.226921 0.279279 0.311172 0.359297 0.380629 0.209705 0.220291 0.148987 0.330806 0.271532 0.367314 0.338187 0.373247 0.381609 0.364601 0.329868 0.348966 0.407858 0.380925 1.000000
In [97]:
import pandas as pd
import numpy as np
from scipy.stats import pearsonr

# Assuming 'df' is your DataFrame
# Create a correlation matrix
correlation_matrix = data_HS_Factor.corr()

# Create an empty DataFrame for p-values
p_values_matrix = pd.DataFrame(np.zeros_like(correlation_matrix)
                               , columns=correlation_matrix.columns
                               , index=correlation_matrix.index)

# Calculate p-values for each pair of variables
for i in range(len(correlation_matrix.columns)):
    for j in range(i+1, len(correlation_matrix.columns)):
        corr, p_value = pearsonr(data_HS_Factor[correlation_matrix.columns[i]], data_HS_Factor[correlation_matrix.columns[j]])
        p_values_matrix.iloc[i, j] = p_value
        p_values_matrix.iloc[j, i] = p_value

# Print the correlation matrix
print("Correlation Matrix:")
display(correlation_matrix.style.applymap(highlight_cell))

# Print the p-values matrix
print("\nP-Values Matrix:")
display(p_values_matrix.style.applymap(highlight_cell))
Correlation Matrix:
Feature Brand Battery back up Keypad Display Sound Size of the screen Electronic media Print media Hoardings Friends Family members Voice clarity MP3 facility Bluetooth Camera pixels FM radio Games Video Memory size Price Stylish
Feature                                          
Brand 1.000000 0.447500 0.368049 0.352448 0.299773 0.219962 0.241112 0.238421 0.202074 0.220499 0.205741 0.189865 0.208792 0.203259 0.226424 0.251729 0.181322 0.219793 0.208544 0.111346 0.285657
Battery back up 0.447500 1.000000 0.455116 0.473122 0.356497 0.275212 0.183919 0.156460 0.128429 0.219719 0.188218 0.313370 0.188104 0.177044 0.175327 0.187393 0.174319 0.151544 0.194127 0.207753 0.226921
Keypad 0.368049 0.455116 1.000000 0.550146 0.463320 0.273884 0.287988 0.297248 0.266476 0.291228 0.211269 0.291135 0.140184 0.163924 0.176070 0.226731 0.243890 0.166411 0.173915 0.153351 0.279279
Display 0.352448 0.473122 0.550146 1.000000 0.515270 0.338573 0.266246 0.291500 0.196218 0.251557 0.241758 0.409060 0.232849 0.224646 0.216971 0.231296 0.245455 0.226117 0.188534 0.153898 0.311172
Sound 0.299773 0.356497 0.463320 0.515270 1.000000 0.411655 0.238118 0.249039 0.177031 0.324270 0.315028 0.401818 0.216129 0.198162 0.210070 0.232918 0.259878 0.212409 0.219263 0.240665 0.359297
Size of the screen 0.219962 0.275212 0.273884 0.338573 0.411655 1.000000 0.378187 0.410941 0.264674 0.290560 0.239298 0.345437 0.305405 0.290902 0.322440 0.288611 0.269994 0.278295 0.346447 0.264013 0.380629
Electronic media 0.241112 0.183919 0.287988 0.266246 0.238118 0.378187 1.000000 0.754414 0.636217 0.293009 0.240326 0.252094 0.373971 0.344800 0.343241 0.319687 0.209542 0.341613 0.323455 0.175582 0.209705
Print media 0.238421 0.156460 0.297248 0.291500 0.249039 0.410941 0.754414 1.000000 0.651765 0.288497 0.240645 0.240040 0.376907 0.353263 0.370847 0.351341 0.284418 0.349543 0.353232 0.182963 0.220291
Hoardings 0.202074 0.128429 0.266476 0.196218 0.177031 0.264674 0.636217 0.651765 1.000000 0.331637 0.177769 0.108796 0.266229 0.247542 0.318332 0.303566 0.236877 0.260842 0.296166 0.117033 0.148987
Friends 0.220499 0.219719 0.291228 0.251557 0.324270 0.290560 0.293009 0.288497 0.331637 1.000000 0.524864 0.303580 0.151109 0.164342 0.159330 0.179428 0.213524 0.151927 0.193085 0.167739 0.330806
Family members 0.205741 0.188218 0.211269 0.241758 0.315028 0.239298 0.240326 0.240645 0.177769 0.524864 1.000000 0.305155 0.141054 0.102727 0.067721 0.154411 0.213426 0.093876 0.161557 0.196582 0.271532
Voice clarity 0.189865 0.313370 0.291135 0.409060 0.401818 0.345437 0.252094 0.240040 0.108796 0.303580 0.305155 1.000000 0.333644 0.282737 0.263019 0.294226 0.320801 0.231258 0.249381 0.243936 0.367314
MP3 facility 0.208792 0.188104 0.140184 0.232849 0.216129 0.305405 0.373971 0.376907 0.266229 0.151109 0.141054 0.333644 1.000000 0.802965 0.730100 0.475656 0.310699 0.720118 0.616411 0.167273 0.338187
Bluetooth 0.203259 0.177044 0.163924 0.224646 0.198162 0.290902 0.344800 0.353263 0.247542 0.164342 0.102727 0.282737 0.802965 1.000000 0.794365 0.445135 0.310691 0.732494 0.585997 0.182164 0.373247
Camera pixels 0.226424 0.175327 0.176070 0.216971 0.210070 0.322440 0.343241 0.370847 0.318332 0.159330 0.067721 0.263019 0.730100 0.794365 1.000000 0.525500 0.347600 0.787034 0.619238 0.192735 0.381609
FM radio 0.251729 0.187393 0.226731 0.231296 0.232918 0.288611 0.319687 0.351341 0.303566 0.179428 0.154411 0.294226 0.475656 0.445135 0.525500 1.000000 0.408284 0.484568 0.424790 0.234218 0.364601
Games 0.181322 0.174319 0.243890 0.245455 0.259878 0.269994 0.209542 0.284418 0.236877 0.213524 0.213426 0.320801 0.310699 0.310691 0.347600 0.408284 1.000000 0.378922 0.346574 0.237919 0.329868
Video 0.219793 0.151544 0.166411 0.226117 0.212409 0.278295 0.341613 0.349543 0.260842 0.151927 0.093876 0.231258 0.720118 0.732494 0.787034 0.484568 0.378922 1.000000 0.633084 0.183343 0.348966
Memory size 0.208544 0.194127 0.173915 0.188534 0.219263 0.346447 0.323455 0.353232 0.296166 0.193085 0.161557 0.249381 0.616411 0.585997 0.619238 0.424790 0.346574 0.633084 1.000000 0.279794 0.407858
Price 0.111346 0.207753 0.153351 0.153898 0.240665 0.264013 0.175582 0.182963 0.117033 0.167739 0.196582 0.243936 0.167273 0.182164 0.192735 0.234218 0.237919 0.183343 0.279794 1.000000 0.380925
Stylish 0.285657 0.226921 0.279279 0.311172 0.359297 0.380629 0.209705 0.220291 0.148987 0.330806 0.271532 0.367314 0.338187 0.373247 0.381609 0.364601 0.329868 0.348966 0.407858 0.380925 1.000000
P-Values Matrix:
Feature Brand Battery back up Keypad Display Sound Size of the screen Electronic media Print media Hoardings Friends Family members Voice clarity MP3 facility Bluetooth Camera pixels FM radio Games Video Memory size Price Stylish
Feature                                          
Brand 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000003 0.000000 0.000002 0.000011 0.000001 0.000003 0.000000 0.000000 0.000028 0.000000 0.000001 0.010527 0.000000
Battery back up 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000022 0.000312 0.003142 0.000000 0.000014 0.000000 0.000014 0.000044 0.000052 0.000015 0.000057 0.000481 0.000007 0.000002 0.000000
Keypad 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000001 0.000000 0.001253 0.000157 0.000048 0.000000 0.000000 0.000124 0.000060 0.000411 0.000000
Display 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000006 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000013 0.000392 0.000000
Sound 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000044 0.000000 0.000000 0.000000 0.000001 0.000005 0.000001 0.000000 0.000000 0.000001 0.000000 0.000000 0.000000
Size of the screen 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
Electronic media 0.000000 0.000022 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000001 0.000000 0.000000 0.000051 0.000001
Print media 0.000000 0.000312 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000024 0.000000
Hoardings 0.000003 0.003142 0.000000 0.000006 0.000044 0.000000 0.000000 0.000000 0.000000 0.000000 0.000041 0.012452 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.007155 0.000601
Friends 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000500 0.000151 0.000240 0.000034 0.000001 0.000466 0.000008 0.000109 0.000000
Family members 0.000002 0.000014 0.000001 0.000000 0.000000 0.000000 0.000000 0.000000 0.000041 0.000000 0.000000 0.000000 0.001168 0.018329 0.120489 0.000374 0.000001 0.031184 0.000196 0.000005 0.000000
Voice clarity 0.000011 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.012452 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
MP3 facility 0.000001 0.000014 0.001253 0.000000 0.000001 0.000000 0.000000 0.000000 0.000000 0.000500 0.001168 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000114 0.000000
Bluetooth 0.000003 0.000044 0.000157 0.000000 0.000005 0.000000 0.000000 0.000000 0.000000 0.000151 0.018329 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000026 0.000000
Camera pixels 0.000000 0.000052 0.000048 0.000000 0.000001 0.000000 0.000000 0.000000 0.000000 0.000240 0.120489 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000008 0.000000
FM radio 0.000000 0.000015 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000034 0.000374 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
Games 0.000028 0.000057 0.000000 0.000000 0.000000 0.000000 0.000001 0.000000 0.000000 0.000001 0.000001 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
Video 0.000000 0.000481 0.000124 0.000000 0.000001 0.000000 0.000000 0.000000 0.000000 0.000466 0.031184 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000023 0.000000
Memory size 0.000001 0.000007 0.000060 0.000013 0.000000 0.000000 0.000000 0.000000 0.000000 0.000008 0.000196 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
Price 0.010527 0.000002 0.000411 0.000392 0.000000 0.000000 0.000051 0.000024 0.007155 0.000109 0.000005 0.000000 0.000114 0.000026 0.000008 0.000000 0.000000 0.000023 0.000000 0.000000 0.000000
Stylish 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000001 0.000000 0.000601 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
In [98]:
import pandas as pd
import numpy as np
from factor_analyzer import FactorAnalyzer
from factor_analyzer.factor_analyzer import calculate_kmo
from factor_analyzer.factor_analyzer import calculate_bartlett_sphericity

# Assuming 'data_HS_Factor' is your DataFrame
# Specify the number of factors
n_factors = 10

# Initialize FactorAnalyzer
fa = FactorAnalyzer(n_factors, rotation=None)

# Fit the model
fa.fit(data_HS_Factor)

# Obtain the factor loadings
factor_loadings = pd.DataFrame(fa.loadings_, index=data_HS_Factor.columns)

# Calculate Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy

kmo_per_variable, kmo_total = calculate_kmo(data_HS_Factor)

# Print the KMO values
print("Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy per variable:")
print(kmo_per_variable)
print("\nKaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy (Total):", kmo_total)

chi2_statistic,p_value = calculate_bartlett_sphericity(data_HS_Factor)

# Print the test statistics
print("\nBartlett's Test of Sphericity:")
print(f"Chi-Square Statistic: {chi2_statistic}")
print(f"P-value: {p_value}")
Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy per variable:
[0.89924419 0.86183536 0.89853055 0.89540982 0.92394103 0.94286954
 0.86282679 0.86729638 0.85747471 0.84080338 0.82888089 0.91518042
 0.9059546  0.89209979 0.90308777 0.95581706 0.93279876 0.91796229
 0.95219443 0.88549424 0.92260799]

Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy (Total): 0.9010115504729133

Bartlett's Test of Sphericity:
Chi-Square Statistic: 5210.445073973882
P-value: 0.0

From the above KMO result, we note that the KMO value is 0.901. From the above recommendations of Kaiser and Rice, it is appropriate to proceed with factor analysis of the data on hand.

3.2.6 Cluster Analysis:¶

Another alternative is to cluster analyze the variables and inspect the dendrogram. This is a good option in present context.

In [99]:
from scipy.cluster.hierarchy import linkage, dendrogram

# Assuming 'data_HS_Factor' is your data matrix
# Transpose the data matrix to cluster on columns
data_transposed = data_HS_Factor.T

# Perform hierarchical clustering with single linkage
linkage_matrix = linkage(data_transposed, method='single')

# Create a dendrogram
#dendrogram(linkage_matrix, orientation='top', labels=data_HS_Factor.columns, distance_sort='descending', show_leaf_counts=True)

# Create a dendrogram with transposed orientation
dendrogram(linkage_matrix, orientation='right', labels=data_HS_Factor.columns
           , distance_sort='descending', show_leaf_counts=True)

# Add labels and title
plt.xlabel('Feature Index')
plt.ylabel('Distance')
plt.title('Dendrogram with Single Linkage on Columns')

# Show the plot
plt.show()

From the Dendrogram, it is clearly evident that there are three groups of variables, signifying different aspects. This suggest that there are correlations between variables as well as the number of factors that can be extracted.

In [100]:
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans

# Assuming 'data_HS_Factor' is your data matrix
# Transpose the data matrix to cluster on columns
data_transposed = data_HS_Factor.T

# Specify the number of clusters (k)
num_clusters = 6  # You can adjust this based on your requirements

# Perform k-means clustering
kmeans = KMeans(n_clusters=num_clusters, random_state=42)
cluster_labels = kmeans.fit_predict(data_transposed)

# Visualize the results
plt.figure(figsize=(10, 6))
plt.scatter(data_transposed.index, cluster_labels, c=cluster_labels, cmap='viridis', marker='o', s=50)
plt.title('K-Means Clustering on Columns')
plt.xticks(rotation=90)
plt.xlabel('Feature Index')
plt.ylabel('Cluster Label')
plt.show()
C:\Users\vkakarla\AppData\Local\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
In [101]:
import pandas as pd
from factor_analyzer import FactorAnalyzer

# Assuming 'data_HS_Factor' is your DataFrame
# Specify the number of factors
n_factors = 3

# Create a FactorAnalyzer object
fa_woer = FactorAnalyzer(len(data_HS_Factor.columns),method='principal',rotation=None) #without extracation and rotation
fa_wer  = FactorAnalyzer(n_factors,method='principal', rotation="varimax")

# Fit the factor model to your data
fa_woer.fit(data_HS_Factor)
fa_wer.fit(data_HS_Factor)

# Get the variable names
features = data_HS_Factor.columns

# Get initial communalities
communalities_without_extraction_rotation = fa_woer.get_communalities()

# Get extracted communalities
communalities_with_extraction_rotation = fa_wer.get_communalities()

# Create a DataFrame with feature, initial communality, and extracted communality
communalities_table = pd.DataFrame({    'Feature': features,    
                                    'Communalities (Initial)': communalities_without_extraction_rotation,
                                    'Communalities (With Extraction and Rotation)': communalities_with_extraction_rotation,
                                   })

# Display the communalities table
# print('\n',features)
# print('\n',communalities_without_rotation)
# print('\n',communalities_with_rotation,'\n')

communalities_table.style.applymap(highlight_cell)
Out[101]:
  Feature Communalities (Initial) Communalities (With Extraction and Rotation)
0 Brand 1.000000 0.305565
1 Battery back up 1.000000 0.448350
2 Keypad 1.000000 0.491550
3 Display 1.000000 0.535471
4 Sound 1.000000 0.534186
5 Size of the screen 1.000000 0.382207
6 Electronic media 1.000000 0.767796
7 Print media 1.000000 0.788377
8 Hoardings 1.000000 0.737310
9 Friends 1.000000 0.386403
10 Family members 1.000000 0.320768
11 Voice clarity 1.000000 0.416320
12 MP3 facility 1.000000 0.754092
13 Bluetooth 1.000000 0.772045
14 Camera pixels 1.000000 0.803458
15 FM radio 1.000000 0.439657
16 Games 1.000000 0.301096
17 Video 1.000000 0.766746
18 Memory size 1.000000 0.600867
19 Price 1.000000 0.196907
20 Stylish 1.000000 0.453419
In [102]:
from factor_analyzer import FactorAnalyzer

# Assuming 'data_HS_Factor' is your DataFrame
# Specify the number of factors
n_factors = 9

# Create a FactorAnalyzer object
fa = FactorAnalyzer(len(data_HS_Factor.columns), method='principal',rotation='varimax')

# Fit the factor model to your data
fa.fit(data_HS_Factor)

# Get the explained variance
explained_variance = fa.get_factor_variance()

# Create a DataFrame with the total variance explained
total_variance_table = pd.DataFrame({
    'Factor': [f'Factor {i+1}' for i in range(len(data_HS_Factor.columns))],
    'SS Loadings': explained_variance[0],
    'Proportion Var': explained_variance[1],
    'Cumulative Var': explained_variance[2]
})

# Display the total variance table
print("Total Variance Explained Table:")
total_variance_table.style.applymap(highlight_cell)
Total Variance Explained Table:
Out[102]:
  Factor SS Loadings Proportion Var Cumulative Var
0 Factor 1 3.527983 0.167999 0.167999
1 Factor 2 0.963806 0.045896 0.213895
2 Factor 3 1.096564 0.052217 0.266112
3 Factor 4 1.033088 0.049195 0.315307
4 Factor 5 1.038610 0.049458 0.364764
5 Factor 6 1.030469 0.049070 0.413834
6 Factor 7 1.027504 0.048929 0.462763
7 Factor 8 1.012489 0.048214 0.510977
8 Factor 9 1.022477 0.048689 0.559666
9 Factor 10 0.962137 0.045816 0.605482
10 Factor 11 1.003432 0.047782 0.653265
11 Factor 12 0.952925 0.045377 0.698642
12 Factor 13 0.987753 0.047036 0.745678
13 Factor 14 0.759410 0.036162 0.781840
14 Factor 15 1.004751 0.047845 0.829686
15 Factor 16 1.014496 0.048309 0.877995
16 Factor 17 1.121274 0.053394 0.931389
17 Factor 18 0.313894 0.014947 0.946336
18 Factor 19 0.681454 0.032450 0.978786
19 Factor 20 0.237971 0.011332 0.990118
20 Factor 21 0.207513 0.009882 1.000000

From the above correlation matrix, comunalities and dendogram we can observe that not all the variables are important for this analysis. We can eliminate these variables and run the analysis again¶

We can keep the below variables which better correlations and communalities

  • Electronic Media
  • Print Media
  • Hoardings
  • MP3 Facility
  • Bluetooth
  • Camera Pixel
  • Video
In [103]:
HS_Influence_Columns_Final = ['HS - Influence Rating for Electronic media',
                               'HS - Influence Rating for Print media',
                               'HS - Influence Rating for Hoardings',
                               'HS - Influence Rating for MP3 facility',
                               'HS - Influence Rating for Bluetooth',
                               'HS - Influence Rating for Camera pixels',
                               'HS - Influence Rating for Video',
                               ]

data_HS_Factor_Final = data2[HS_Influence_Columns_Final]
data_HS_Factor_Final.columns
Out[103]:
Index(['HS - Influence Rating for Electronic media',
       'HS - Influence Rating for Print media',
       'HS - Influence Rating for Hoardings',
       'HS - Influence Rating for MP3 facility',
       'HS - Influence Rating for Bluetooth',
       'HS - Influence Rating for Camera pixels',
       'HS - Influence Rating for Video'],
      dtype='object', name='Feature')
In [104]:
data_HS_Factor_Final.columns = data_HS_Factor_Final.columns.str.replace('HS - Influence Rating for ', '')
print(data_HS_Factor_Final.columns)
data_HS_Factor
Index(['Electronic media', 'Print media', 'Hoardings', 'MP3 facility',
       'Bluetooth', 'Camera pixels', 'Video'],
      dtype='object', name='Feature')
Out[104]:
Feature Brand Battery back up Keypad Display Sound Size of the screen Electronic media Print media Hoardings Friends Family members Voice clarity MP3 facility Bluetooth Camera pixels FM radio Games Video Memory size Price Stylish
0 5 4 4 4 3 3 3 2 2 4 4 5 3 3 3 4 4 2 4 5 4
1 3 3 5 4 4 5 4 4 4 5 3 3 1 1 1 5 3 1 2 4 5
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341 5 2 5 5 5 2 2 2 2 4 4 3 2 2 2 3 2 2 2 5 1
342 5 2 5 3 5 5 2 2 2 5 5 5 4 4 4 4 4 4 5 5 5
343 5 5 4 4 4 3 2 2 2 4 3 3 2 4 2 4 2 3 4 4 4
344 5 5 3 5 5 3 3 3 3 5 3 5 3 3 3 3 3 3 3 5 3
345 5 5 5 2 2 4 2 2 3 4 2 2 2 2 3 3 3 3 3 3 3
346 5 4 4 4 4 3 3 2 2 4 4 4 4 4 4 3 4 4 3 3 4
347 3 4 3 4 3 5 2 2 2 4 4 4 2 2 2 2 3 2 2 4 3
348 5 4 2 4 3 3 2 2 4 4 4 4 2 2 2 2 4 2 4 3 4
349 4 5 4 3 4 3 4 3 3 5 4 5 1 1 1 1 1 1 1 5 4
350 4 5 3 4 4 4 1 1 1 4 5 5 1 1 1 1 4 1 4 4 4
351 5 5 5 5 5 4 1 1 1 5 5 4 1 1 1 1 1 1 5 5 5
352 5 5 5 5 4 4 2 2 1 3 5 5 4 4 4 4 4 4 4 5 5
353 5 4 4 4 4 4 4 4 3 3 5 5 3 2 3 4 5 3 3 2 3
354 4 4 3 4 4 3 2 2 2 4 5 4 2 2 2 2 4 2 2 5 3
355 5 5 4 4 3 4 2 2 2 3 4 4 2 2 2 5 4 2 2 5 4
356 3 3 3 4 4 4 2 2 1 3 5 4 1 1 1 1 3 1 1 5 4
357 5 5 4 5 4 5 2 2 2 3 5 4 5 5 5 4 4 5 5 4 5
358 5 5 4 4 4 4 2 2 2 2 4 4 2 2 2 4 4 2 2 4 3
359 5 5 5 4 4 4 2 2 2 2 4 4 4 4 5 5 4 5 5 4 5
360 4 4 4 4 4 4 2 2 2 2 4 4 2 2 2 4 4 2 2 4 4
361 4 4 5 5 4 5 2 2 2 2 5 2 5 5 5 5 5 5 5 4 5
362 4 4 5 5 5 3 3 3 3 4 4 4 3 4 3 4 4 3 3 3 3
363 4 4 3 3 4 4 1 1 1 4 5 4 2 2 2 2 2 2 2 4 4
364 4 4 4 5 5 5 5 4 3 4 4 4 3 2 1 4 4 3 2 3 4
365 4 4 4 4 4 4 1 1 1 4 5 4 1 1 1 1 3 1 1 4 4
366 5 5 2 2 5 5 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
367 4 4 4 4 2 3 3 2 2 4 4 4 4 4 4 2 4 4 2 3 2
368 4 4 4 4 4 4 4 2 2 2 2 4 4 4 4 2 4 4 4 2 2
369 3 3 3 3 3 3 2 2 5 5 5 5 1 1 1 1 5 2 3 3 5
370 3 3 3 3 3 3 3 2 3 5 5 4 1 1 1 1 4 1 3 3 4
371 3 2 3 3 3 3 3 2 3 5 5 3 1 3 1 1 1 1 3 5 5
372 4 3 3 4 4 4 2 2 2 4 4 4 2 2 2 2 2 4 4 4 4
373 5 4 5 5 3 5 4 4 3 5 5 5 2 2 1 1 4 1 5 1 5
374 5 5 5 5 5 4 1 1 1 4 4 4 1 1 1 1 4 1 1 4 4
375 4 3 2 4 1 3 5 5 4 3 2 1 5 3 2 1 2 3 1 3 2
376 4 4 4 4 4 4 3 3 3 4 4 4 3 3 4 5 5 5 4 4 4
377 4 5 5 5 5 3 3 3 3 4 4 4 5 4 4 5 4 4 3 3 5
378 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
379 4 4 4 4 4 4 2 2 2 2 4 4 2 2 2 4 4 2 2 4 4
380 3 4 4 4 3 4 4 4 2 4 4 4 4 4 4 4 4 4 4 4 2
381 4 4 4 4 4 4 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4
382 1 4 4 2 5 5 2 2 3 4 5 4 1 1 1 1 3 1 1 4 4
383 5 4 4 2 5 5 2 2 2 5 5 3 5 5 5 5 5 5 4 5 5
384 4 4 4 4 4 4 2 2 2 4 4 4 2 2 2 2 2 2 2 2 4
385 4 4 4 4 4 4 3 1 1 3 4 3 1 1 1 4 1 1 1 5 4
386 5 5 5 5 5 2 2 2 2 3 3 4 1 1 1 1 1 1 1 1 1
387 2 2 1 1 1 1 1 1 1 1 1 4 1 1 1 5 5 1 1 5 5
388 4 3 3 3 4 4 1 1 1 4 5 4 1 1 1 4 4 1 1 4 4
389 5 5 4 4 4 4 1 1 1 5 5 5 1 1 1 4 4 1 1 5 5
390 4 4 4 4 4 5 1 1 1 5 5 5 4 4 4 4 4 4 4 4 5
391 5 5 4 4 4 4 1 1 1 3 5 4 1 1 1 1 3 1 1 5 5
392 4 4 4 4 4 4 1 1 1 4 4 4 1 1 1 1 4 1 1 4 4
393 5 5 5 5 4 4 1 1 1 5 5 4 1 1 1 4 4 1 1 4 4
394 5 5 4 4 4 5 1 1 1 5 5 5 1 1 1 4 4 1 1 4 4
395 4 5 5 5 5 4 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4
396 5 5 4 3 4 4 4 4 4 4 4 3 5 5 5 5 3 4 4 4 5
397 5 5 4 4 4 4 5 4 4 4 4 4 4 3 3 2 4 4 3 4 4
398 4 4 5 4 4 5 4 4 3 4 4 4 5 5 4 3 3 4 3 4 5
399 3 3 3 4 4 4 3 3 3 3 4 4 4 4 4 4 4 4 4 4 3
400 4 4 4 4 4 1 1 1 1 1 1 4 1 1 1 4 4 1 1 4 4
401 4 4 4 4 4 4 1 1 1 4 4 4 4 4 4 4 4 5 5 1 5
402 5 1 4 4 4 1 1 1 3 3 3 4 4 4 5 5 2 4 3 1 4
403 5 5 5 5 5 3 2 1 1 4 4 4 1 1 1 1 3 2 2 4 3
404 5 3 3 3 5 5 2 2 1 4 2 4 4 4 4 1 4 4 2 2 5
405 5 5 5 5 4 1 1 1 1 4 4 1 1 1 1 1 4 2 2 5 4
406 5 5 5 5 5 1 1 1 1 3 3 4 2 2 1 4 4 1 1 4 4
407 3 3 4 4 3 1 1 1 1 4 4 3 1 1 1 1 1 1 1 3 1
408 5 5 5 5 5 1 1 1 1 4 4 4 1 1 1 5 5 2 2 4 4
409 5 5 5 5 5 5 1 1 1 4 4 4 1 1 1 4 4 1 1 5 4
410 5 5 5 5 5 1 1 1 1 4 3 4 1 1 1 4 3 1 1 3 5
411 5 5 5 5 5 5 1 5 1 5 3 4 4 4 4 4 4 4 1 3 5
412 5 5 5 5 5 1 1 1 1 3 4 4 3 3 3 3 3 4 1 4 5
413 4 5 4 5 5 3 3 4 2 4 4 5 3 4 4 5 4 3 5 5 4
414 4 3 4 4 5 4 4 4 4 5 4 5 4 4 3 5 4 4 4 5 4
415 5 4 3 4 3 3 2 1 1 3 3 5 4 2 2 5 4 4 5 5 2
416 5 4 5 4 4 3 4 3 3 4 5 4 4 3 3 4 3 4 3 4 4
417 5 5 1 3 5 4 1 1 1 3 3 4 3 4 1 1 1 3 3 3 3
418 3 5 5 5 4 5 3 3 4 2 2 5 3 3 3 5 5 3 5 5 3
419 5 5 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3
420 4 4 4 5 4 4 4 3 2 4 4 4 4 5 5 5 5 5 4 4 4
421 1 2 3 4 3 4 3 3 2 2 2 3 5 5 5 5 5 5 4 3 5
422 4 5 4 5 5 4 4 4 4 5 4 5 4 4 4 5 5 5 5 3 4
423 5 3 4 3 3 4 4 3 4 5 4 3 5 5 5 5 4 4 4 3 5
424 5 5 3 5 5 5 5 2 3 4 4 5 5 5 5 3 3 4 4 4 5
425 5 5 5 5 5 5 5 5 5 4 4 5 5 5 5 5 4 5 5 1 5
426 4 3 5 5 5 5 3 3 5 2 3 4 3 3 3 4 3 3 3 3 4
427 1 5 5 5 5 4 2 2 2 5 3 5 4 4 4 5 4 4 3 5 1
428 5 4 4 4 4 4 3 3 3 4 4 4 3 3 3 3 3 3 3 4 3
429 5 5 4 4 3 3 2 2 4 4 4 4 3 3 4 4 3 4 4 4 5
430 5 5 4 4 3 3 2 2 4 5 3 3 4 3 4 4 2 4 4 2 4
431 4 4 2 4 4 4 4 2 5 5 5 4 2 2 2 2 2 2 2 4 4
432 5 5 4 5 5 4 5 5 3 5 3 5 3 3 3 4 4 3 3 5 5
433 5 5 4 5 5 4 4 4 4 5 4 5 5 5 5 5 5 5 5 4 4
434 1 2 2 2 3 4 1 1 2 4 2 4 1 1 1 1 1 1 1 5 1
435 4 3 3 2 4 4 2 1 2 1 1 2 3 2 4 4 3 2 3 4 2
436 4 3 3 4 3 2 2 2 4 1 1 4 1 1 4 4 4 4 2 4 2
437 5 4 4 5 5 5 2 2 4 4 3 4 5 5 5 5 5 5 4 4 5
438 4 3 4 2 2 4 3 3 4 4 1 2 1 1 1 4 3 1 4 4 1
439 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3 4 5 3 5 5
440 5 5 5 5 5 5 5 5 4 5 4 5 5 5 5 5 5 5 5 5 5
441 4 4 4 4 4 4 3 3 3 4 4 3 3 3 3 3 4 4 4 4 4
442 5 5 5 3 3 3 3 3 3 3 3 3 3 3 3 4 3 3 3 5 3
443 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
444 5 5 5 5 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
445 5 5 5 5 4 4 4 3 3 4 5 5 2 2 2 2 3 1 1 4 4
446 4 5 4 4 4 4 2 2 2 4 4 4 1 1 1 4 4 3 4 3 4
447 5 4 4 4 4 5 4 4 2 4 4 4 3 3 3 4 4 4 3 4 4
448 4 4 4 4 5 4 3 3 3 4 4 4 5 2 3 5 5 3 4 3 4
449 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
450 4 4 4 4 4 4 4 4 4 4 4 4 1 1 1 1 4 1 1 4 4
451 5 5 4 4 5 5 3 3 3 3 3 3 3 3 3 5 2 2 4 4 4
452 5 5 4 4 5 4 5 5 2 3 4 5 5 5 5 5 5 5 5 5 5
453 5 5 4 5 5 4 4 4 4 4 5 4 4 4 4 4 4 4 4 4 4
454 4 4 3 4 4 4 4 4 2 3 4 4 4 4 4 4 4 4 4 4 4
455 4 3 1 1 5 5 5 5 1 5 5 4 4 4 4 5 1 4 4 5 4
456 5 5 5 5 5 4 3 4 3 3 3 3 3 3 2 3 2 2 5 5 3
457 5 5 5 5 4 3 3 3 3 5 5 4 4 3 3 5 4 3 4 4 4
458 5 5 5 5 4 3 3 3 3 3 3 4 3 3 3 4 3 4 4 5 4
459 4 4 4 4 4 3 2 2 2 4 4 3 2 2 2 2 3 2 2 3 3
460 5 4 4 4 4 4 2 2 2 3 3 5 4 4 4 5 4 4 4 4 4
461 4 4 3 3 3 3 3 3 3 4 4 4 4 3 3 3 3 3 3 4 3
462 5 5 5 5 5 5 5 5 2 5 5 5 2 2 2 2 5 2 2 5 5
463 5 5 4 4 4 4 5 4 4 4 4 5 4 5 5 5 5 4 4 4 5
464 5 5 5 5 5 5 3 3 3 5 5 4 4 5 5 4 4 4 5 5 4
465 5 5 5 5 5 5 5 5 5 5 5 5 1 1 1 5 5 1 1 5 5
466 5 5 4 4 5 5 3 3 3 3 3 5 5 5 5 5 5 5 5 5 5
467 5 4 4 3 4 4 3 3 3 4 4 4 2 2 4 3 3 3 4 3 3
468 5 5 5 5 5 5 4 4 3 3 4 5 5 5 5 5 3 3 5 5 5
469 5 5 4 4 4 3 3 3 3 4 3 4 3 3 3 3 3 3 2 3 4
470 4 4 4 4 4 3 3 3 3 3 3 5 5 5 5 5 4 4 4 5 5
471 4 5 3 4 3 5 2 3 3 3 3 4 2 2 2 4 4 2 2 4 3
472 5 3 3 3 4 4 3 3 4 4 2 2 2 2 2 2 4 3 3 3 3
473 4 3 4 4 5 4 4 2 2 4 4 4 4 4 2 2 3 3 3 2 4
474 5 5 5 5 5 5 5 5 5 5 4 5 5 5 5 3 5 5 5 5 5
475 5 4 3 5 4 4 4 1 1 4 4 5 5 5 4 4 3 4 3 2 3
476 5 5 4 3 4 4 4 4 4 4 4 4 1 1 4 4 4 4 2 2 2
477 4 5 3 4 4 5 5 2 3 4 4 4 5 4 5 3 4 4 5 4 5
478 5 2 5 2 5 2 5 2 5 5 5 2 2 2 2 2 2 2 2 5 5
479 4 5 4 4 5 4 3 3 3 5 5 4 1 1 1 4 3 1 4 4 4
480 4 5 3 3 3 2 2 2 2 3 5 3 4 3 3 3 3 3 2 3 3
481 5 5 5 5 5 5 5 4 4 5 5 5 2 2 2 5 4 2 2 4 4
482 4 4 4 5 5 5 5 5 4 4 4 5 5 1 1 5 4 1 4 5 5
483 4 4 3 3 4 3 2 2 2 3 5 3 3 2 2 3 2 3 2 3 3
484 4 5 4 4 4 4 3 3 3 4 4 4 3 3 3 4 3 3 3 4 4
485 4 5 4 4 4 3 3 3 3 3 5 5 3 3 2 4 3 4 3 4 4
486 4 5 3 5 4 5 5 3 4 4 3 3 5 4 3 5 3 3 2 3 3
487 4 5 4 4 4 3 3 3 3 4 5 5 4 3 2 4 3 3 2 3 4
488 5 3 4 5 4 5 3 4 4 5 5 5 1 1 5 5 1 1 3 4 5
489 5 4 4 4 4 3 3 3 4 4 4 4 4 3 4 4 4 4 4 4 4
490 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
491 5 4 3 3 5 3 2 2 3 4 5 3 3 3 4 3 2 3 3 3 3
492 4 4 4 5 5 5 4 4 4 4 4 4 5 5 4 4 4 4 5 4 4
493 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5
494 5 5 5 5 5 5 4 4 4 5 5 5 4 4 4 5 5 5 4 4 5
495 5 5 5 5 5 5 5 5 4 4 5 5 2 1 1 5 1 1 1 4 3
496 4 4 4 4 4 3 3 3 3 3 3 3 3 4 2 4 3 4 3 4 3
497 5 4 4 4 4 3 4 3 4 3 4 3 4 3 2 4 3 3 4 3 3
498 4 4 4 3 3 3 3 3 3 4 5 4 4 3 3 4 3 3 3 2 4
499 4 5 3 5 4 2 1 2 2 3 3 4 2 2 2 2 4 1 3 5 3
500 5 5 4 4 4 3 3 3 3 3 3 5 2 1 5 4 4 1 1 4 3
501 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 4 3 4 4 4
502 4 3 4 5 4 5 4 4 4 4 3 4 2 2 4 4 3 3 3 3 4
503 5 4 4 5 2 4 3 5 2 1 4 5 4 2 4 1 5 5 5 4 3
504 5 5 5 5 5 5 5 5 4 5 5 5 5 4 5 5 5 5 5 3 5
505 5 4 3 3 3 4 3 4 1 3 4 3 5 3 4 5 4 5 5 5 4
506 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 5 5 4 3
507 5 5 5 5 5 5 5 5 4 5 4 5 5 3 5 5 5 5 5 5 5
508 3 5 4 4 4 3 3 3 3 4 4 4 4 3 3 3 3 3 4 4 3
509 4 4 4 4 4 2 1 1 1 3 3 4 4 4 4 4 4 3 4 4 4
510 5 5 5 5 5 1 4 4 4 4 4 4 4 4 4 4 4 4 4 1 4
511 5 5 3 4 3 3 3 3 5 3 3 2 2 2 2 3 3 2 2 4 3
512 4 4 4 4 4 4 4 4 4 4 4 3 3 4 3 4 4 3 3 3 3
513 4 5 4 5 5 3 3 2 2 3 3 5 4 4 4 3 3 3 3 4 3
514 5 5 5 5 5 5 1 1 1 1 1 5 5 5 5 1 1 5 5 3 4
515 4 3 3 3 3 2 3 3 3 3 4 4 3 3 3 3 3 3 5 4 4
516 5 5 5 5 5 5 5 3 4 3 3 5 5 5 5 3 5 5 5 5 5
517 5 5 5 4 5 5 4 4 4 4 4 5 5 5 4 4 4 4 4 4 5
518 4 4 4 4 4 4 3 3 3 3 4 3 2 2 2 5 5 2 3 4 4
519 5 4 4 4 4 4 4 4 4 4 4 2 2 2 2 5 5 2 3 4 4
520 4 3 4 4 4 3 3 3 3 4 4 4 2 2 2 2 4 2 3 3 4
521 4 4 4 4 4 3 3 3 3 4 4 3 2 2 2 4 4 2 3 4 4
522 4 4 4 4 4 4 3 3 3 4 4 3 2 2 2 5 5 2 3 4 5
523 5 4 4 4 4 3 5 3 3 3 5 4 2 2 2 5 5 2 3 4 4
524 4 4 4 4 4 4 3 3 3 3 3 4 2 2 2 2 4 1 3 4 4
525 4 4 4 4 4 4 4 4 3 4 4 3 2 2 2 3 3 3 3 3 3
526 5 5 5 5 5 4 4 4 4 5 5 4 4 4 4 5 5 4 4 4 5
In [105]:
round(data_HS_Factor_Final.corr(),3).style.applymap(highlight_cell)
Out[105]:
Feature Electronic media Print media Hoardings MP3 facility Bluetooth Camera pixels Video
Feature              
Electronic media 1.000000 0.754000 0.636000 0.374000 0.345000 0.343000 0.342000
Print media 0.754000 1.000000 0.652000 0.377000 0.353000 0.371000 0.350000
Hoardings 0.636000 0.652000 1.000000 0.266000 0.248000 0.318000 0.261000
MP3 facility 0.374000 0.377000 0.266000 1.000000 0.803000 0.730000 0.720000
Bluetooth 0.345000 0.353000 0.248000 0.803000 1.000000 0.794000 0.732000
Camera pixels 0.343000 0.371000 0.318000 0.730000 0.794000 1.000000 0.787000
Video 0.342000 0.350000 0.261000 0.720000 0.732000 0.787000 1.000000
In [106]:
import pandas as pd
import numpy as np
from scipy.stats import pearsonr

# Assuming 'df' is your DataFrame
# Create a correlation matrix
correlation_matrix = data_HS_Factor_Final.corr()

# Create an empty DataFrame for p-values
p_values_matrix = pd.DataFrame(np.zeros_like(correlation_matrix), columns=correlation_matrix.columns, index=correlation_matrix.index)

# Calculate p-values for each pair of variables
for i in range(len(correlation_matrix.columns)):
    for j in range(i+1, len(correlation_matrix.columns)):
        corr, p_value = pearsonr(data_HS_Factor[correlation_matrix.columns[i]], data_HS_Factor[correlation_matrix.columns[j]])
        p_values_matrix.iloc[i, j] = p_value
        p_values_matrix.iloc[j, i] = p_value

# Print the correlation matrix
print("Correlation Matrix:")
display(correlation_matrix.style.applymap(highlight_cell))

# Print the p-values matrix
print("\nP-Values Matrix:")
display(round(p_values_matrix,3))
Correlation Matrix:
Feature Electronic media Print media Hoardings MP3 facility Bluetooth Camera pixels Video
Feature              
Electronic media 1.000000 0.754414 0.636217 0.373971 0.344800 0.343241 0.341613
Print media 0.754414 1.000000 0.651765 0.376907 0.353263 0.370847 0.349543
Hoardings 0.636217 0.651765 1.000000 0.266229 0.247542 0.318332 0.260842
MP3 facility 0.373971 0.376907 0.266229 1.000000 0.802965 0.730100 0.720118
Bluetooth 0.344800 0.353263 0.247542 0.802965 1.000000 0.794365 0.732494
Camera pixels 0.343241 0.370847 0.318332 0.730100 0.794365 1.000000 0.787034
Video 0.341613 0.349543 0.260842 0.720118 0.732494 0.787034 1.000000
P-Values Matrix:
Feature Electronic media Print media Hoardings MP3 facility Bluetooth Camera pixels Video
Feature
Electronic media 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Print media 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Hoardings 0.0 0.0 0.0 0.0 0.0 0.0 0.0
MP3 facility 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Bluetooth 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Camera pixels 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Video 0.0 0.0 0.0 0.0 0.0 0.0 0.0

3.2.3 Anti-Image Matrix¶

It is a matrix of negative partial covariances and correlations. They can give an indication of correlations which aren’t due to the common factors. Small values indicate that your variables are relatively free of unexplained correlations. Most or all values off the diagonal should be small (close to zero). Each value on the diagonal of the anti-image correlation matrix shows the Measure of Sampling Adequacy (MSA) for the respective item. Values less than 0.5 may indicate variables that do not seem to fit with the structure of the other variables. Consider dropping such variables from your analysis.

3.2.4 Kaiser-Meyer -Olkin Measure of Sampling Adequacy:¶

This table shows two tests which indicate the suitability of your data for factor analysis. One can examine Kaiser’s measure of overall sampling adequacy and a measure of the sampling adequacy for each indicator. This measure, Kaiser-Meyer -Olkin Measure of Sampling Adequacy, is a popular diagnostic measure. KMO provides a measure to assess the extent to which the indicators of a construct belong together. That is, it is a measure of homogeneity of variables. There are no statistical tests for the KMO measure. The following guidelines are suggested(by Kaiser and Rice):

KMO Measure Recommendation
≥ 0.9 Marvelous
0.80+ Meritorious
0.70+ Middling
0.60+ Mediocre
0.50+ Miserable
< 0.50 Unacceptable

This criterion is accurate when there are less than 30 variables and communalities after extraction are greater than 0.7 or when the sample size exceeds 250 and the average communality is greater than 0.6.

3.2.5 Bartlett’s Test of Sphericity¶

It tests the null hypothesis that whether the original correlation matrix is an identity matrix, which would indicate that your variables are unrelated. For factor analysis to work we need some relationships between variables and if the correlation matrix were an identity matrix then all correlation coefficients would be zero. Therefore, we want this test to be significant. The significance level gives the result of the test. Very small values (p-value less than .05) indicate that there are probably significant relationships among your variables. A significant test tells that the correlation matrix is not an identity matrix and hence, we expect some relationships between the variables. If a value is more than 0.10 then it may indicate that the data are not suitable for factor analysis.

For the problem under analysis, the significance value is 0.000, and hence the variables are not independent. So, this test also suggests us to proceed with the factor analysis.

In [107]:
import pandas as pd
import numpy as np
from factor_analyzer import FactorAnalyzer
from factor_analyzer.factor_analyzer import calculate_kmo
from factor_analyzer.factor_analyzer import calculate_bartlett_sphericity

# Assuming 'data_HS_Factor' is your DataFrame
# Specify the number of factors
n_factors = 10

# Initialize FactorAnalyzer
fa = FactorAnalyzer(n_factors, rotation=None)

# Fit the model
fa.fit(data_HS_Factor_Final)

# Obtain the factor loadings
factor_loadings = pd.DataFrame(fa.loadings_, index=data_HS_Factor_Final.columns)

# Calculate Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy

kmo_per_variable, kmo_total = calculate_kmo(data_HS_Factor_Final)

# Print the KMO values
print("Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy per variable:")
print(kmo_per_variable)
print("\nKaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy (Total):", kmo_total)

chi2_statistic,p_value = calculate_bartlett_sphericity(data_HS_Factor_Final)

# Print the test statistics
print("\nBartlett's Test of Sphericity:")
print(f"Chi-Square Statistic: {chi2_statistic}")
print(f"P-value: {p_value}")
Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy per variable:
[0.78757719 0.78915079 0.83392998 0.87329581 0.83859726 0.84347744
 0.88024368]

Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy (Total): 0.8379828211326764

Bartlett's Test of Sphericity:
Chi-Square Statistic: 2571.370279462248
P-value: 0.0

From the above KMO result, we note that the KMO value is 0.838. From the above recommendations of Kaiser and Rice, it is appropriate to proceed with factor analysis of the data on hand.

In [108]:
from scipy.cluster.hierarchy import linkage, dendrogram

# Assuming 'data_HS_Factor' is your data matrix
# Transpose the data matrix to cluster on columns
data_transposed = data_HS_Factor_Final.T

# Perform hierarchical clustering with single linkage
linkage_matrix = linkage(data_transposed, method='single')

# Create a dendrogram
#dendrogram(linkage_matrix, orientation='top', labels=data_HS_Factor.columns, distance_sort='descending', show_leaf_counts=True)

# Create a dendrogram with transposed orientation
dendrogram(linkage_matrix, orientation='right', labels=data_HS_Factor_Final.columns
           , distance_sort='descending', show_leaf_counts=True)

# Add labels and title
plt.xlabel('Feature Index')
plt.ylabel('Distance')
plt.title('Dendrogram with Single Linkage on Columns')

# Show the plot
plt.show()

From the Dendrogram, it is clearly evident that there are two groups of variables, signifying different aspects. This suggest that there are correlations between variables as well as the number of factors that can be extracted.

In [109]:
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans

# Assuming 'data_HS_Factor' is your data matrix
# Transpose the data matrix to cluster on columns
data_transposed = data_HS_Factor_Final.T

# Specify the number of clusters (k)
num_clusters = 2  # You can adjust this based on your requirements

# Perform k-means clustering
kmeans = KMeans(n_clusters=num_clusters, random_state=42)
cluster_labels = kmeans.fit_predict(data_transposed)

# Visualize the results
plt.figure(figsize=(6, 4))
plt.scatter(data_transposed.index, cluster_labels, c=cluster_labels, cmap='viridis', marker='o', s=50)
plt.title('K-Means Clustering on Columns')
plt.xticks(rotation=90)
plt.xlabel('Feature Index')
plt.ylabel('Cluster Label')
plt.show()
C:\Users\vkakarla\AppData\Local\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
In [110]:
import matplotlib.pyplot as plt
import numpy as np
from factor_analyzer import FactorAnalyzer

# Assuming 'data_HS_Factor' is your DataFrame
# Specify a range of factor numbers
num_factors_range = range(1, len(data_HS_Factor_Final.columns) + 1)

# Create an empty list to store eigenvalues
eigenvalues = []

# Iterate over different numbers of factors
for n_factors in num_factors_range:
    fa = FactorAnalyzer(n_factors, method='principal', rotation='varimax')
    fa.fit(data_HS_Factor_Final)
    eigenvalues.append(fa.get_eigenvalues()[0][n_factors - 1])  # Extract the eigenvalue for the specified number of factors

# Plot the scree plot
plt.plot(num_factors_range, eigenvalues, marker='o')
plt.title('Scree Plot')
plt.xlabel('Number of Factors')
plt.ylabel('Eigenvalues')
plt.show()
C:\Users\vkakarla\AppData\Local\anaconda3\lib\site-packages\factor_analyzer\factor_analyzer.py:663: UserWarning: No rotation will be performed when the number of factors equals 1.
  warnings.warn(

3.2.7 Extraction of Factors¶

Factor analysis consists of selecting the method of extracting the components, the number of components to be extracted, and the method of rotation for interpretation of the factors. We have selected the Principal Component Method of extraction and the varimax method of rotation. The number of factors extracted is based on eigenvalue more than one rule. This has resulted in several tables and we consider them one by one.

3.2.8 Communalities Table:¶

This table gives us the proportion of variance explained by the underlying factors. After extraction some of the factors are discarded and so some information is lost. The amount of variance in each variable that can be explained by the retained factors is represented by the communalities after extraction.

In [111]:
import pandas as pd
from factor_analyzer import FactorAnalyzer

# Assuming 'data_HS_Factor' is your DataFrame
# Specify the number of factors
n_factors = 2

#The rotation must be one of the following: 
#['varimax', 'oblimax', 'quartimax', 'equamax', 'geomin_ort', 'promax', 'oblimin', 'quartimin', 'geomin_obl', None]
# Defaults to ‘promax’.

# Create a FactorAnalyzer object
fa_woer = FactorAnalyzer(len(data_HS_Factor_Final.columns),method='principal',rotation=None) #without extracation and rotation
fa_wer  = FactorAnalyzer(n_factors,method='principal', rotation='varimax')
 
# Fit the factor model to your data
fa_woer.fit(data_HS_Factor_Final)
fa_wer.fit(data_HS_Factor_Final)

# Get the variable names
features = data_HS_Factor_Final.columns

# Get initial communalities
communalities_without_extraction_rotation = fa_woer.get_communalities()

# Get extracted communalities
communalities_with_extraction_rotation = fa_wer.get_communalities()

# Create a DataFrame with feature, initial communality, and extracted communality
communalities_table = pd.DataFrame({    'Feature': features,    
                                    'Communalities (Initial)': communalities_without_extraction_rotation,
                                    'Communalities (With Extraction and Rotation)': communalities_with_extraction_rotation,
                                   })

# Display the communalities table
# print('\n',features)
# print('\n',communalities_without_rotation)
# print('\n',communalities_with_rotation,'\n')

communalities_table.style.applymap(highlight_cell)
Out[111]:
  Feature Communalities (Initial) Communalities (With Extraction and Rotation)
0 Electronic media 1.000000 0.806702
1 Print media 1.000000 0.818329
2 Hoardings 1.000000 0.743834
3 MP3 facility 1.000000 0.805195
4 Bluetooth 1.000000 0.848188
5 Camera pixels 1.000000 0.833526
6 Video 1.000000 0.797564

From the table we note that all the variables have their communalities above 0.744. Remember, we have already eliminated the unimportant variables from the analysis.

3.2.9 Total Variance Explained Table:¶

This table gives eigenvalues, variance explained, and cumulative variance explained for your factor solution. The first panel gives values based on initial eigenvalues. For the initial solution, there are as many components or factors as there are variables. The ”Total” column gives the amount of variance in the observed variables accounted for by each component or factor. The ”% of Variance” column gives the percent of variance accounted for by each specific factor or component, relative to the total variance in all the variables. The ”Cumulative %” column gives the percent of variance accounted for by all factors or components up to and including the current one. For instance the Cumulative % for the second factor is the sum of the % of Variance for the first and second factors. In a good factor analysis, there are a few factors that explain a lot of the variance and the rest of the factors explain relatively small amounts of variance. The Extraction Sums of Squared Loadings group gives information regarding the extracted factors or components. For principal components extraction, these values will be the same as those reported under Initial Eigenvalues. For other extraction methods, these values will generally be smaller than the initial values, due to errors in measurements. The variance accounted for by rotated factors or components may be different from those reported for the extraction but the Cumulative % for the set of factors or components will always be the same.

In [112]:
import pandas as pd
from factor_analyzer import FactorAnalyzer

# Assuming 'data_HS_Factor' is your DataFrame
# Specify the number of factors
n_factors = 2

# Create a FactorAnalyzer object
fa = FactorAnalyzer(n_factors, method='principal', rotation='varimax')

# Fit the factor model to your data
fa.fit(data_HS_Factor_Final)

# Get the explained variance
explained_variance = fa.get_factor_variance()

# Get additional information
initial_eigenvalues = fa.get_eigenvalues()[0]
percentage_of_variance = initial_eigenvalues / sum(initial_eigenvalues) * 100
cumulative_percentage_of_variance = percentage_of_variance.cumsum()

rotation_sums_squared_loadings = fa.get_factor_variance()[0]
percentage_of_rotation_sums_squared_loadings = fa.get_factor_variance()[1]*100
cumulative_percentage_of_rotation_sums_squared_loadings = fa.get_factor_variance()[2]*100

intial_variance_table = pd.DataFrame({
    'Component': [f'{i + 1}' for i in range(len(data_HS_Factor_Final.columns))],
    'Initial Eigenvalues': initial_eigenvalues,
    'Initial Eigenvalues (% of Variance)': percentage_of_variance,
    'Initial Eigenvalues (%Cumulative of Variance)': cumulative_percentage_of_variance,

})

factor_variance_table = pd.DataFrame({
    'Component': [f'{i + 1}' for i in range(n_factors)],
    'Rotation SS Loadings': rotation_sums_squared_loadings,
    'Rotation SS Loadings (% of Variance)': percentage_of_rotation_sums_squared_loadings,
    'Rotation SS Loadings (Cumulative of Variance)': cumulative_percentage_of_rotation_sums_squared_loadings
})

# Combine both dataframes
combined_table = pd.merge(intial_variance_table, factor_variance_table, on='Component', how='left')

# Display the combined table
print("Total Variance Explained Table:")
round(combined_table,3)
Total Variance Explained Table:
Out[112]:
Component Initial Eigenvalues Initial Eigenvalues (% of Variance) Initial Eigenvalues (%Cumulative of Variance) Rotation SS Loadings Rotation SS Loadings (% of Variance) Rotation SS Loadings (Cumulative of Variance)
0 1 4.058 57.967 57.967 3.221 46.021 46.021
1 2 1.596 22.795 80.762 2.432 34.741 80.762
2 3 0.404 5.768 86.530 NaN NaN NaN
3 4 0.307 4.385 90.916 NaN NaN NaN
4 5 0.248 3.538 94.453 NaN NaN NaN
5 6 0.224 3.200 97.653 NaN NaN NaN
6 7 0.164 2.347 100.000 NaN NaN NaN

From the table we note that there are two eigenvalues, namely, 4.058, 1.596 whose values exceed 1. These factors account for 80.762% of the total variation. Individually, the first factor explains 57.967%, and the second factor explains 22.795% of the total variation.

3.2.10 Number of Factors To Be Extracted¶

There are quite a number guiding rules to determine the number of factors to be extracted. Some of the popular criteria are

  • Eigenvalue greater than one rule
  • Scree Plot
  • Total Variance Explained

3.2.11 Components Matrix¶

The Component Matrix gives the estimated factor loadings. The elements of this matrix describe the covariances or the correlations between the manifest variables and the latent common factors depending on whether the covariance matrix or correlation matrix is involved in the analysis. The sum of squares of the row elements of Component Matrix gives the communality of the corresponding variable. Using which we can estimate the specific variances of the manifest variables. Similarly, the sum of squares of the column elements of Component Matrix gives the eigenvalues of the covariance/correlation matrix. These values help in the computation of the proportion of variance explained by each factor. The method of estimation used to get the Component Matrix is the Principal Component method of estimation. This is referred to as unrotated factor solution.

In [113]:
import pandas as pd
from factor_analyzer import FactorAnalyzer

# Assuming 'data_HS_Factor' is your DataFrame
# Specify the number of factors
n_factors = 2

# Create a FactorAnalyzer object
fa = FactorAnalyzer(n_factors,method='principal', rotation=None)

# Fit the factor model to your data
fa.fit(data_HS_Factor_Final)

# Get the component matrix
component_matrix = pd.DataFrame(fa.loadings_, index=data_HS_Factor_Final.columns
                                , columns=[f'Factor {i + 1}' for i in range(n_factors)])

# Display the component matrix
print("Component Matrix:")
component_matrix.style.applymap(highlight_cell)
Component Matrix:
Out[113]:
  Factor 1 Factor 2
Feature    
Electronic media 0.674567 0.593010
Print media 0.687301 0.588172
Hoardings 0.585451 0.633310
MP3 facility 0.833684 -0.331914
Bluetooth 0.838281 -0.381408
Camera pixels 0.847734 -0.338931
Video 0.819231 -0.355561

We now try to interpret the factor solution by examining the elements of this Component Matrix. From this matrix we observe that except for one element all the rest of the elements are loaded on the first factor. However, common sense says that they does not belong to a single category. This makes interpretation of the factors difficult. To facilitate the interpretation of the factors, we consider the varimax rotation. This has resulted in the following rotated solution.

3.2.12 Rotated Component Matrix¶

From this Rotated Component Matrix, we note that all the variables have got partitioned into two mutually exclusive groups and are clearly interpretable. The first group of variables signifies the features of the mobile and the second group can be called a media factor. This explains how the rotation of initial factor solution is useful in the interpretation of factors.

In [114]:
import pandas as pd
from factor_analyzer import FactorAnalyzer

# Assuming 'data_HS_Factor' is your DataFrame
# Specify the number of factors
n_factors = 2

# Create a FactorAnalyzer object
fa = FactorAnalyzer(n_factors, method='principal', rotation='varimax')

# Fit the factor model to your data
fa.fit(data_HS_Factor_Final)

# Get the component matrix
component_matrix = pd.DataFrame(fa.loadings_, index=data_HS_Factor_Final.columns
                                , columns=[f'Factor {i + 1}' for i in range(n_factors)])

# Display the component matrix
print("Rotated Component Matrix:")
component_matrix.style.applymap(highlight_cell)
Rotated Component Matrix:
Out[114]:
  Factor 1 Factor 2
Feature    
Electronic media 0.202574 0.875023
Print media 0.215742 0.878513
Hoardings 0.106670 0.855836
MP3 facility 0.870908 0.216135
Bluetooth 0.903489 0.178595
Camera pixels 0.886415 0.218622
Video 0.872945 0.188497
In [ ]:
 

Chapter 4¶

Analysis of Usage of Mobile Phones¶

4.1 Introduction¶

In this section we attempt to study our second objective, namely What for students are using mobiles? To study this, we have formulated a question with 13 variables and we felt that these variables are useful in order to study the above mentioned objective. A student may using a mobile, because all his friends are using, or he is feeling secured in case of emergency, or he may be using to communicate with his family and friends. To measure the usage of a mobile we have formulated the following questions.

  • I use cell phone because all my friends uses (All my friends use).
  • I look old fashioned without it (Old fashioned ).
  • It brings me prestige in my environment (Prestige).
  • I feel secured in case of emergency (Secure).
  • I like showing the features of my phone to people around me(Show-off).
  • It matches my lifestyle (Life style).
  • I need it for urgent communication(Communication).
  • I make use of most of the facilities available with my phone(Facilities).
  • It makes my life easier(Life easier).
  • Mostly use to call friends and family (Call Friends and Family).
  • Mostly use to SMS (Mostly use SMS).
  • Mostly use to listen music / to watch videos (Music or Videos).
  • Mostly use for games (Games).

We now consider factor analysis to analyze the data for these questions. Principal Component Method is used to extract the factors. Eigenvalue more than one rule is used to fix the number of factors to be extracted. we rotated the factors using the varimax rotation.

In [115]:
Usage_Columns = ['All my friends use',
                                'Old fashioned',
                                'Prestige',
                                'Secure',
                                'Show-off',
                                'Life style',
                                'Communication',
                                'Facilities',
                                'Life easier',
                                'Call Friends and Family',
                                'Mostly use SMS',
                                'Music or Videos',
                                'Games'
                               ]

data_Usage_Factor = data2[Usage_Columns].dropna()
data_Usage_Factor
Out[115]:
Feature All my friends use Old fashioned Prestige Secure Show-off Life style Communication Facilities Life easier Call Friends and Family Mostly use SMS Music or Videos Games
0 3 2 2 4 3 4 4 4 4 4.0 2 3 2
1 5 1 3 5 2 5 5 3 4 5.0 3 2 3
2 4 2 2 5 2 2 5 4 4 5.0 4 4 3
3 4 4 3 4 4 2 4 4 4 4.0 4 5 4
4 4 4 4 4 4 5 5 5 5 5.0 4 5 5
5 5 4 3 4 2 2 4 5 4 4.0 5 5 3
6 1 4 4 5 1 1 5 5 1 1.0 1 1 1
7 2 2 2 5 3 5 5 4 4 5.0 3 4 4
8 4 4 4 4 4 5 4 5 4 4.0 3 4 4
9 2 1 1 5 2 4 5 4 4 5.0 3 4 2
10 3 3 2 4 3 3 5 3 2 3.0 2 3 2
11 3 2 1 5 3 4 5 5 5 5.0 3 5 3
12 4 2 2 5 4 4 5 4 4 5.0 2 4 4
13 4 3 2 5 2 2 5 4 3 3.0 3 2 1
14 3 3 3 4 2 2 5 5 3 5.0 4 5 3
15 4 4 4 3 2 2 4 2 3 5.0 5 2 3
16 1 1 1 1 1 1 5 1 1 1.0 1 1 1
17 3 3 2 5 1 2 5 4 4 3.0 3 3 2
18 2 3 2 5 2 3 5 4 4 3.0 3 2 2
19 2 3 2 5 2 2 5 4 4 3.0 3 2 3
20 1 1 1 3 1 1 5 3 5 3.0 1 1 1
21 5 5 5 2 5 5 2 3 2 2.0 5 3 2
22 4 3 2 5 4 4 4 4 4 3.0 2 2 2
23 3 4 3 4 4 5 5 3 4 4.0 5 4 4
24 2 2 2 5 3 3 5 3 3 3.0 5 3 2
25 2 2 2 5 2 2 5 5 5 5.0 3 3 3
26 4 4 2 5 3 5 5 4 5 5.0 4 4 4
27 4 1 1 5 3 5 5 4 5 4.0 4 3 3
28 3 2 3 5 3 4 5 4 3 5.0 4 4 3
29 3 3 4 5 1 2 5 3 5 5.0 2 1 1
30 4 3 3 4 3 4 5 5 5 5.0 3 5 4
31 1 1 1 3 1 1 3 3 4 4.0 3 3 2
32 4 3 3 5 2 3 5 3 4 5.0 3 4 1
33 5 3 4 3 5 5 5 5 5 5.0 5 5 5
34 3 3 3 4 3 4 3 5 3 5.0 4 3 3
35 5 4 3 4 5 3 4 5 4 3.0 5 4 3
36 4 3 4 2 3 4 3 5 3 5.0 2 4 2
37 4 4 5 5 4 3 5 5 3 4.0 5 4 5
38 4 4 4 3 2 1 1 1 1 2.0 3 4 5
39 3 3 3 3 3 3 3 3 3 3.0 3 3 3
40 3 3 3 1 1 5 1 4 4 4.0 2 4 2
41 4 3 3 3 3 3 3 3 3 3.0 3 3 3
42 2 2 1 1 2 3 4 5 3 2.0 3 2 1
43 3 3 3 3 3 3 3 3 3 3.0 3 3 3
44 2 1 2 5 1 3 5 5 4 5.0 2 3 3
45 2 2 2 5 2 3 5 4 4 5.0 5 4 4
46 4 3 5 4 4 2 5 2 1 3.0 5 2 4
47 5 4 5 4 5 4 5 4 4 4.0 5 4 5
48 4 3 3 4 3 4 5 3 2 3.0 2 3 2
49 5 4 4 4 5 4 5 4 3 5.0 4 4 4
50 3 3 3 5 3 4 4 3 2 3.0 3 4 4
51 4 2 3 4 4 4 4 4 4 4.0 4 4 4
52 5 4 3 3 4 5 5 5 4 3.0 5 5 4
53 5 4 5 5 5 3 3 3 5 5.0 5 5 3
54 5 4 5 4 5 4 3 5 4 5.0 4 5 5
55 5 5 5 4 5 4 5 4 5 4.0 5 4 5
56 5 5 5 4 5 4 5 5 4 5.0 5 4 5
57 5 4 5 3 2 3 5 1 5 4.0 5 3 5
58 3 4 3 5 4 3 5 4 4 4.0 3 4 4
59 5 2 5 5 4 4 4 5 5 5.0 4 5 5
60 5 2 2 4 2 2 4 2 4 4.0 2 2 2
61 4 5 3 5 5 5 5 5 4 4.0 3 3 3
62 5 4 5 3 5 3 5 2 5 1.0 5 5 3
63 5 4 5 3 5 3 5 3 5 2.0 5 1 3
64 5 4 5 4 5 4 3 5 3 5.0 3 5 2
65 5 4 4 3 4 3 5 5 4 4.0 2 2 4
66 3 4 3 5 3 2 5 3 4 4.0 5 5 3
67 5 5 5 4 5 4 5 5 4 5.0 5 3 3
68 3 4 3 5 3 2 5 4 5 4.0 5 4 3
69 4 2 1 4 4 2 4 4 4 4.0 4 4 2
70 4 2 3 4 4 4 5 4 3 4.0 2 1 2
71 4 2 2 4 2 3 4 3 4 5.0 4 3 2
72 2 3 2 4 2 4 5 5 4 5.0 4 4 5
73 5 3 3 5 5 5 5 5 4 5.0 5 3 5
74 3 2 2 3 2 2 4 3 2 4.0 2 2 2
75 2 2 2 5 3 2 5 5 3 5.0 4 4 3
76 4 2 2 5 2 2 4 2 2 5.0 5 2 2
77 4 4 1 5 4 4 5 4 4 4.0 4 3 3
78 4 2 2 4 2 2 4 4 4 4.0 4 3 3
79 4 3 4 3 3 3 4 3 3 5.0 4 4 3
80 5 3 4 5 4 5 3 5 4 4.0 5 3 5
81 2 2 2 4 2 4 4 4 4 4.0 3 3 2
82 5 4 3 5 3 4 1 3 1 1.0 1 3 5
83 3 3 1 5 1 1 5 4 1 5.0 4 1 2
84 5 4 3 5 4 2 3 3 2 2.0 5 2 1
85 4 4 4 4 4 4 4 4 4 4.0 4 4 4
86 3 2 2 5 4 3 5 4 4 4.0 3 4 4
87 3 2 2 4 1 2 4 4 3 4.0 4 2 1
88 3 2 2 5 2 3 5 4 4 4.0 2 2 2
89 5 4 4 5 3 3 5 5 5 5.0 4 4 3
90 5 5 4 5 3 5 3 2 4 2.0 4 3 4
91 4 4 4 5 4 3 4 5 5 4.0 5 4 4
92 4 3 3 2 3 4 5 4 3 3.0 4 4 4
93 3 1 1 5 1 1 5 2 2 5.0 1 2 1
94 4 4 3 3 5 2 5 2 2 3.0 1 5 3
95 2 2 2 5 3 2 4 4 4 4.0 2 2 2
96 4 2 4 4 3 4 4 4 4 5.0 5 2 1
97 3 5 5 5 3 5 5 5 5 5.0 5 5 5
98 5 3 4 5 3 4 3 1 4 5.0 2 3 5
99 2 3 3 4 4 3 4 4 3 4.0 2 3 2
100 1 4 1 5 3 3 5 4 4 4.0 4 3 2
101 5 3 2 5 3 4 5 5 5 5.0 5 5 4
102 4 3 3 4 3 3 5 5 4 5.0 3 4 3
103 2 2 2 5 2 5 5 4 5 5.0 2 2 2
104 2 2 2 5 2 2 5 2 2 5.0 2 2 2
105 3 4 4 5 1 1 5 1 5 5.0 1 1 1
106 3 4 3 4 4 3 4 4 4 3.0 4 4 4
107 4 3 5 4 2 3 3 3 4 4.0 2 3 2
108 4 2 2 3 1 2 5 4 3 4.0 3 3 2
109 2 2 2 5 5 5 5 5 5 5.0 5 5 5
110 2 2 2 5 2 2 4 5 3 4.0 4 4 1
111 3 3 2 4 2 2 5 4 3 5.0 1 1 2
112 4 3 4 4 5 5 5 4 3 4.0 4 2 2
113 3 3 3 5 3 3 5 4 4 4.0 4 5 4
114 2 2 4 5 4 4 5 4 4 5.0 4 4 3
115 3 3 2 3 3 2 3 2 3 5.0 3 2 2
116 5 4 4 4 3 3 5 5 4 5.0 3 4 3
117 3 3 4 5 5 5 5 5 5 5.0 5 5 3
118 3 3 3 4 3 4 4 4 4 5.0 4 4 2
119 2 2 2 5 4 4 5 5 5 5.0 4 4 3
120 2 3 2 5 3 4 5 5 4 5.0 1 2 2
121 4 3 4 4 4 4 4 4 4 4.0 4 4 4
122 5 5 4 4 3 4 5 4 3 5.0 3 5 3
123 5 3 5 5 3 5 4 5 5 5.0 5 5 5
124 2 4 2 5 2 2 5 4 4 4.0 3 4 2
125 5 5 4 5 3 3 5 5 5 5.0 3 3 3
126 5 3 2 5 2 3 5 5 4 5.0 2 2 2
127 2 4 1 5 1 1 5 5 5 5.0 4 1 2
128 1 1 1 1 1 1 1 1 1 1.0 1 1 1
129 5 5 5 5 5 5 5 5 5 5.0 5 5 5
130 5 5 5 5 5 5 5 5 5 5.0 5 5 5
131 2 1 1 3 1 1 5 5 4 4.0 1 1 1
132 4 2 1 5 1 2 5 2 3 5.0 1 2 2
133 1 1 1 4 3 3 5 3 3 3.0 2 1 1
134 3 4 2 3 5 5 4 4 3 4.0 4 3 1
135 3 1 1 5 1 3 5 2 5 5.0 3 2 2
136 5 3 2 5 1 1 5 5 4 5.0 3 2 1
137 5 3 2 4 2 2 4 3 3 5.0 4 3 2
138 3 5 4 3 5 2 5 4 3 3.0 5 4 3
139 3 3 2 4 2 4 5 5 5 5.0 5 3 2
140 2 2 2 4 2 4 5 4 4 4.0 3 4 3
141 2 3 2 4 2 3 4 3 3 4.0 3 2 3
142 4 3 4 5 3 3 5 4 5 5.0 5 2 2
143 2 2 2 4 2 2 4 4 2 4.0 4 2 2
144 5 3 4 5 2 3 5 4 4 5.0 2 5 5
145 5 5 4 5 5 5 5 5 5 5.0 5 4 3
146 2 2 1 5 1 2 5 5 5 5.0 5 2 2
147 5 4 4 5 4 4 5 5 4 5.0 4 4 4
148 4 4 3 5 4 4 5 5 4 5.0 3 5 5
149 1 1 1 5 1 1 5 1 4 5.0 1 1 1
150 2 3 2 4 2 2 4 3 2 4.0 3 2 4
151 5 5 3 4 4 3 5 5 5 5.0 5 3 3
152 2 2 2 5 2 5 5 5 5 5.0 5 4 3
153 3 3 3 4 2 3 4 4 4 4.0 3 3 2
154 4 5 1 4 1 1 5 5 5 5.0 4 1 1
155 3 4 4 5 4 4 4 4 4 5.0 4 3 3
156 4 4 2 4 2 4 5 4 4 3.0 2 2 2
157 3 2 3 4 3 4 4 4 4 4.0 3 4 2
158 2 2 2 4 3 2 5 2 2 4.0 2 2 1
159 2 2 1 4 2 2 4 4 4 4.0 3 2 2
160 3 4 3 4 4 3 4 4 4 4.0 4 4 3
161 3 3 2 5 2 3 5 4 3 5.0 5 4 4
162 2 5 2 5 2 2 5 5 5 5.0 2 5 4
163 4 3 2 5 5 4 5 5 5 5.0 5 5 4
164 2 2 4 5 5 5 5 5 5 5.0 2 5 5
165 5 5 5 4 5 4 5 4 5 5.0 5 4 2
166 5 1 2 5 4 4 5 5 4 5.0 5 3 3
167 2 2 2 5 2 2 5 5 5 5.0 5 2 5
168 4 2 2 5 3 3 5 4 3 5.0 5 3 3
169 2 2 2 5 2 2 5 5 5 5.0 5 5 5
170 4 2 2 5 2 3 5 4 4 4.0 2 3 3
171 4 3 3 4 2 2 4 4 4 5.0 5 5 2
172 3 2 2 5 3 4 5 4 4 5.0 4 2 2
173 2 2 1 5 2 2 5 4 4 4.0 5 3 3
174 2 3 2 4 3 2 5 5 4 5.0 5 4 3
175 2 4 2 5 2 2 5 5 5 5.0 5 5 5
176 2 1 1 5 1 1 5 5 5 5.0 1 5 1
177 2 2 3 4 2 4 5 3 4 5.0 5 2 5
178 2 2 2 4 2 3 4 4 4 4.0 3 4 4
179 4 3 5 5 5 5 5 5 4 4.0 5 4 3
180 5 5 5 5 2 5 3 5 5 5.0 5 2 2
181 3 2 2 4 2 2 4 4 4 4.0 3 2 2
182 4 1 1 5 1 1 5 4 4 5.0 4 5 4
183 4 2 2 4 2 2 4 4 3 4.0 4 5 4
184 2 3 2 4 2 2 5 4 3 4.0 4 4 5
185 1 1 1 4 1 3 3 4 4 4.0 3 2 1
186 2 4 2 4 1 1 5 5 2 5.0 2 1 2
187 2 2 2 5 3 5 5 5 5 5.0 5 5 5
188 5 4 1 5 2 1 1 3 1 3.0 3 5 4
189 1 1 1 5 5 5 5 5 5 5.0 3 5 3
190 3 3 2 5 3 5 5 5 5 5.0 3 5 3
191 2 2 2 5 2 5 5 5 5 5.0 3 5 3
192 5 5 5 5 5 5 5 5 5 5.0 5 5 4
193 4 4 4 5 5 5 5 5 5 5.0 5 5 5
194 4 3 3 5 5 5 5 5 5 5.0 5 5 5
195 2 2 2 5 5 5 5 5 5 5.0 5 5 5
196 2 2 2 4 5 5 5 5 5 5.0 5 5 5
197 2 2 2 2 2 5 5 5 5 5.0 5 5 5
198 2 2 2 5 5 5 5 5 5 5.0 5 5 3
199 3 3 3 5 5 5 5 5 5 5.0 5 5 3
200 3 4 2 4 2 4 4 4 4 4.0 4 4 3
201 3 3 2 4 2 3 4 4 4 4.0 4 4 4
202 2 2 2 4 2 4 4 2 2 4.0 4 2 2
203 5 2 2 4 2 2 5 5 3 5.0 5 2 2
204 5 2 3 5 1 4 5 5 5 5.0 5 1 1
206 3 2 2 5 2 2 5 3 2 5.0 3 2 2
207 5 2 2 5 2 5 5 5 5 4.0 4 4 5
208 4 3 4 4 4 5 4 3 4 3.0 3 3 3
209 3 3 5 5 5 5 5 5 3 5.0 3 3 3
210 4 2 2 4 2 4 4 4 4 4.0 4 2 4
211 1 1 1 5 1 1 5 4 3 4.0 3 1 1
212 4 4 3 3 4 4 5 5 5 5.0 5 5 5
213 2 2 3 4 2 2 4 3 3 4.0 3 2 2
214 3 3 2 5 1 5 5 4 4 4.0 4 3 4
215 3 3 2 1 2 4 4 4 4 4.0 2 2 4
216 2 4 2 4 3 3 4 4 2 4.0 2 4 4
217 5 3 2 2 2 2 4 5 3 4.0 5 2 1
218 3 2 1 5 5 4 5 3 3 5.0 1 3 5
219 4 3 4 5 3 4 5 5 5 5.0 4 4 2
220 4 4 4 4 4 2 4 4 2 4.0 2 2 2
221 3 2 2 5 1 2 5 4 3 5.0 4 2 4
222 3 2 2 4 2 2 4 4 2 4.0 4 2 3
223 3 2 2 4 2 2 4 4 2 2.0 3 3 3
224 3 2 3 4 1 3 5 3 3 5.0 2 1 1
225 4 2 2 5 2 2 4 3 3 3.0 3 3 3
226 2 2 3 5 1 2 4 4 4 5.0 5 4 3
227 2 2 3 5 2 3 5 5 5 5.0 4 5 5
228 2 2 2 5 1 1 5 3 4 2.0 4 3 4
229 4 4 2 4 2 2 4 3 2 4.0 2 2 2
230 2 2 2 4 2 2 4 3 2 4.0 2 2 2
231 2 2 2 4 2 2 4 2 2 4.0 2 4 2
232 4 4 2 4 2 2 4 4 4 4.0 4 4 4
233 5 4 2 4 2 4 5 5 4 5.0 5 4 5
234 2 5 2 5 2 1 5 2 1 5.0 5 3 1
235 2 3 2 5 2 3 4 4 2 5.0 3 3 1
236 2 4 2 4 2 2 5 5 2 5.0 4 3 4
237 2 4 2 1 2 2 4 4 2 1.0 2 3 2
238 3 3 2 4 2 3 4 4 3 4.0 3 3 3
239 2 1 1 4 1 1 4 4 2 4.0 4 2 3
240 3 4 5 5 2 4 3 4 4 4.0 4 4 4
241 2 2 2 4 2 2 4 3 2 4.0 4 2 2
242 4 3 3 4 4 4 4 4 4 4.0 3 3 4
243 2 2 2 4 2 2 4 4 2 4.0 2 2 4
244 4 4 2 5 5 4 5 5 5 5.0 3 5 3
245 3 3 5 5 5 5 5 5 5 5.0 3 5 2
246 2 4 2 4 2 2 4 4 3 2.0 2 2 2
247 1 1 1 5 4 4 5 5 4 5.0 4 4 4
248 2 2 2 5 3 4 5 5 3 4.0 4 4 4
249 3 3 3 5 5 5 5 5 5 5.0 4 5 3
250 4 3 3 3 5 3 5 5 5 5.0 5 5 3
251 4 3 3 5 5 5 5 5 5 5.0 3 5 3
252 3 3 2 5 3 2 5 5 3 3.0 3 4 2
253 2 2 2 5 4 4 2 5 5 5.0 5 5 5
254 2 5 5 5 2 4 5 5 5 5.0 5 5 5
255 2 2 2 5 3 4 5 5 5 5.0 5 5 5
256 5 5 5 5 5 4 5 5 5 5.0 5 5 5
257 2 2 2 5 2 4 5 5 5 5.0 5 5 5
258 5 5 5 5 5 5 5 5 5 5.0 5 5 5
259 2 2 2 5 3 5 5 5 5 5.0 5 5 5
260 2 5 5 5 3 5 5 5 5 5.0 5 5 5
261 2 2 2 5 3 5 5 5 5 5.0 5 5 5
262 2 2 5 5 5 5 5 5 5 5.0 5 5 5
263 3 1 1 4 1 1 5 3 3 5.0 2 5 1
264 1 2 2 5 3 3 5 5 4 4.0 4 3 4
265 5 4 3 3 2 2 4 5 3 5.0 5 5 3
266 2 2 2 4 3 2 3 3 3 4.0 2 3 3
267 3 2 1 5 3 2 5 4 4 4.0 3 2 4
268 3 1 2 5 3 2 5 4 3 3.0 3 1 3
269 1 1 1 5 1 1 5 5 5 5.0 3 5 5
270 1 1 1 5 1 1 5 3 1 5.0 4 1 3
271 5 4 4 4 4 4 4 4 2 4.0 2 2 4
272 3 2 1 5 2 2 5 4 3 5.0 5 5 3
273 2 1 1 4 1 1 4 4 4 4.0 4 4 4
274 1 1 2 5 2 2 4 2 3 4.0 3 4 2
275 3 4 2 5 2 2 5 4 5 5.0 4 2 2
276 4 1 1 5 5 5 5 5 5 5.0 5 5 5
277 2 2 1 5 4 1 5 5 4 5.0 3 3 4
278 3 2 2 4 2 4 4 4 4 4.0 4 4 4
279 2 4 2 5 1 2 5 4 4 4.0 4 2 4
280 5 4 2 5 2 4 5 4 3 5.0 3 3 4
281 1 1 1 4 2 2 4 3 3 3.0 3 3 4
282 4 4 2 4 2 4 4 4 2 4.0 5 4 3
283 2 2 4 5 2 2 5 4 2 3.0 5 1 3
284 4 4 5 5 4 4 5 3 4 5.0 4 4 4
285 2 4 2 4 2 2 4 4 4 4.0 4 4 4
286 4 4 2 4 2 2 3 4 2 4.0 3 4 4
287 3 4 2 4 2 2 4 4 2 4.0 5 2 2
288 2 2 3 4 2 4 4 4 4 4.0 4 4 4
289 2 2 2 4 2 2 4 4 4 4.0 2 4 4
290 2 2 2 4 2 2 4 4 4 4.0 4 4 4
291 2 2 2 4 2 2 4 4 4 4.0 4 4 4
292 4 3 4 4 2 3 1 4 3 3.0 3 4 3
293 5 4 2 5 5 4 4 3 3 4.0 5 5 4
294 2 5 2 5 2 2 5 5 5 5.0 4 4 5
295 2 4 2 4 2 4 4 4 4 4.0 2 2 4
296 5 4 2 5 5 5 5 5 5 5.0 5 5 4
297 2 2 2 3 3 3 3 4 4 4.0 5 5 5
298 2 3 4 3 2 3 4 3 5 5.0 4 4 5
299 4 3 3 3 3 4 4 4 4 4.0 4 4 2
300 4 3 3 4 4 3 4 4 3 4.0 2 2 4
301 3 4 3 5 3 4 3 2 3 5.0 4 4 5
302 2 2 4 5 5 2 4 4 5 4.0 5 4 3
303 5 2 4 4 4 3 4 4 4 5.0 5 5 4
304 2 2 4 5 5 2 5 5 2 2.0 4 3 4
305 2 2 4 5 5 2 5 5 4 4.0 5 4 4
306 4 2 3 4 2 2 4 3 2 4.0 4 2 2
307 3 2 2 4 1 2 4 4 4 4.0 5 2 4
308 4 3 4 4 3 4 4 2 4 4.0 4 3 3
309 4 3 4 5 3 3 4 4 4 4.0 4 4 3
310 2 2 2 4 4 4 4 4 4 4.0 4 2 2
311 4 4 4 4 2 4 4 5 5 5.0 5 4 4
312 4 4 4 5 2 4 5 5 4 5.0 5 5 5
313 3 2 2 4 2 4 4 4 4 4.0 4 3 2
314 5 2 2 5 1 1 5 5 5 5.0 5 4 3
315 5 2 2 4 1 2 4 5 4 5.0 5 5 2
316 4 4 3 4 2 2 4 2 3 4.0 4 4 4
317 5 4 4 5 2 3 5 5 3 5.0 3 2 3
318 1 1 1 5 2 4 5 5 5 5.0 5 5 5
319 1 1 1 5 2 3 5 5 3 5.0 4 1 1
320 1 1 1 4 1 1 4 1 1 4.0 3 4 4
321 2 2 2 5 2 2 5 3 3 5.0 4 4 3
322 3 2 1 3 2 2 3 4 2 3.0 4 3 4
323 3 2 1 3 2 2 3 4 2 3.0 4 3 4
324 2 2 2 4 2 4 4 4 4 4.0 4 4 4
325 2 1 2 5 1 2 5 4 2 4.0 4 4 3
326 4 4 2 4 2 2 4 4 4 2.0 2 2 2
327 3 3 3 3 4 4 4 4 4 4.0 4 3 3
328 1 1 1 4 2 2 4 4 4 4.0 4 4 2
329 5 3 3 5 5 3 5 4 5 2.0 5 5 1
330 5 4 3 5 4 5 2 5 3 2.0 3 1 1
331 5 3 3 3 4 3 3 4 2 2.0 4 2 2
332 4 4 3 5 2 2 5 5 5 5.0 2 2 5
333 3 3 3 5 3 3 5 3 5 3.0 1 1 3
334 5 2 2 2 2 3 2 5 2 4.0 2 1 1
335 5 2 2 5 5 5 5 5 4 5.0 3 5 5
336 4 5 2 5 5 4 1 3 1 2.0 5 4 1
337 4 3 2 5 5 4 5 3 3 4.0 4 5 5
338 4 2 4 3 4 4 4 2 4 4.0 4 5 5
339 3 4 4 3 3 3 3 2 2 4.0 1 1 3
340 4 2 4 5 4 4 4 2 4 4.0 4 5 5
341 4 1 1 5 3 3 5 5 3 5.0 3 2 4
342 5 2 3 5 4 3 2 1 3 3.0 5 5 5
343 3 2 1 5 4 2 5 4 3 3.0 5 3 3
344 4 2 2 5 3 5 4 5 4 5.0 5 3 3
345 3 3 2 5 1 3 5 5 4 5.0 3 3 2
346 5 3 3 3 2 2 2 4 2 4.0 4 4 2
347 5 2 2 5 3 2 4 4 3 5.0 5 4 4
348 4 2 2 4 2 3 4 4 4 4.0 4 2 4
349 5 4 3 5 2 5 5 4 5 5.0 5 2 4
350 4 4 3 5 3 5 5 5 4 5.0 3 3 4
351 4 4 2 5 3 4 5 5 4 5.0 4 4 3
352 4 3 2 5 3 3 5 5 4 4.0 4 4 4
353 3 2 1 5 1 1 5 3 3 5.0 4 1 3
354 3 3 3 5 4 4 5 5 3 5.0 3 2 5
355 4 4 2 5 5 3 5 5 3 5.0 3 4 4
356 4 3 3 5 4 4 5 5 5 5.0 4 3 3
357 4 4 3 5 4 4 5 5 4 5.0 4 3 3
358 4 4 3 4 4 3 5 5 4 5.0 3 4 4
359 3 4 2 5 4 4 5 4 4 4.0 3 3 3
360 3 4 3 4 3 3 4 4 3 4.0 4 4 4
361 2 4 2 4 4 3 4 2 4 4.0 5 5 5
362 2 2 2 5 2 4 4 4 4 4.0 4 4 3
363 4 3 2 5 4 4 5 5 5 5.0 3 3 3
364 4 4 3 5 5 5 5 4 4 4.0 5 5 3
365 4 3 2 5 3 5 5 5 5 5.0 5 3 3
366 2 2 2 4 2 2 4 2 2 4.0 2 2 2
367 4 2 4 4 2 4 4 4 4 4.0 4 4 4
368 2 2 2 4 2 2 4 4 2 4.0 4 2 2
369 3 2 3 5 2 3 5 5 5 3.0 5 3 5
370 3 2 3 5 2 3 5 5 5 3.0 5 3 5
371 3 2 3 5 2 3 5 5 5 3.0 5 4 3
372 4 2 3 5 2 2 5 5 5 3.0 4 5 5
373 3 3 2 5 2 2 5 4 3 5.0 4 3 5
374 4 3 4 4 3 2 3 3 4 4.0 2 4 3
375 3 1 2 3 5 3 4 1 4 5.0 1 2 5
376 4 2 2 4 2 2 4 4 4 4.0 4 4 4
377 5 4 4 5 2 4 4 4 5 4.0 5 5 2
378 4 2 2 5 2 3 5 5 5 5.0 4 4 3
379 2 2 2 5 2 3 5 5 5 5.0 4 4 3
380 4 2 4 5 5 4 4 4 2 4.0 4 4 4
381 4 2 5 5 5 4 4 4 2 4.0 4 4 4
382 4 2 4 4 4 4 4 2 2 5.0 4 2 4
383 5 3 2 5 2 5 5 5 5 5.0 2 5 5
384 4 2 2 4 2 4 4 4 2 4.0 2 2 4
385 4 3 1 5 3 4 4 2 4 4.0 3 3 1
386 4 3 1 4 1 1 4 1 1 5.0 2 2 3
387 2 2 2 5 1 2 5 1 1 5.0 3 1 1
388 5 3 2 5 2 4 5 5 5 5.0 4 2 4
389 4 3 1 5 3 5 5 5 5 5.0 5 3 4
390 4 4 2 5 3 3 5 5 5 5.0 4 4 4
391 4 3 1 5 5 5 5 5 5 5.0 4 3 3
392 5 4 2 5 3 5 5 5 5 5.0 5 4 4
393 5 5 3 5 3 5 5 5 5 5.0 5 4 4
394 5 4 2 5 5 5 5 5 3 5.0 4 3 3
395 1 1 1 3 1 2 4 3 3 4.0 3 4 3
396 3 4 3 4 4 3 4 5 4 5.0 3 3 2
397 4 3 2 4 2 3 4 5 4 4.0 4 3 4
398 2 2 3 5 2 3 4 3 4 4.0 3 3 3
399 3 1 2 4 2 2 4 3 2 3.0 4 4 3
400 2 2 2 5 1 2 5 1 1 5.0 3 1 1
401 4 4 2 5 1 3 5 5 5 4.0 5 5 1
402 3 4 1 4 1 3 5 4 3 3.0 1 4 4
403 4 4 2 5 4 4 5 4 3 4.0 3 4 3
404 5 3 5 5 1 1 4 3 1 3.0 3 4 2
405 4 4 1 4 1 2 5 4 1 4.0 5 1 3
406 1 1 1 3 1 1 3 3 1 3.0 4 1 3
407 4 3 1 4 1 1 4 2 2 5.0 2 1 1
408 3 3 2 5 1 1 4 2 2 3.0 1 1 3
409 3 3 2 5 1 1 4 2 2 3.0 5 5 3
410 4 1 1 4 3 1 3 1 1 4.0 4 1 3
411 1 1 1 3 2 2 3 4 1 4.0 4 4 4
412 3 1 1 3 1 1 4 2 2 3.0 3 3 3
413 3 4 1 4 3 2 4 2 2 5.0 3 4 4
414 4 3 2 5 2 2 5 4 4 5.0 4 4 3
415 4 3 5 5 2 3 3 4 5 5.0 2 1 1
416 3 3 3 4 3 4 4 4 3 4.0 3 4 2
417 1 1 1 3 1 2 5 3 4 4.0 1 3 1
418 5 4 2 2 2 4 3 4 4 5.0 5 2 5
419 3 3 3 4 3 3 4 5 4 4.0 4 4 2
420 2 4 2 4 2 4 4 4 4 4.0 4 4 4
421 1 3 3 5 3 4 5 5 5 5.0 5 5 2
422 5 2 4 3 5 5 3 5 4 3.0 4 4 4
423 4 4 3 5 5 4 5 5 4 5.0 4 4 4
424 1 3 1 4 3 2 3 5 4 5.0 3 5 4
425 2 2 1 5 1 1 5 3 3 4.0 4 3 3
426 3 2 1 3 4 3 3 4 2 4.0 3 3 3
427 1 5 1 5 1 1 5 2 3 5.0 5 2 4
428 4 4 3 5 4 4 4 5 4 5.0 4 4 4
429 4 3 2 5 4 3 5 4 3 4.0 5 4 2
430 4 2 2 5 5 4 4 4 3 5.0 5 5 4
431 3 2 2 5 3 2 4 4 3 5.0 4 5 2
432 3 4 3 5 5 3 5 3 3 5.0 5 3 2
433 5 4 2 5 4 4 4 4 4 5.0 5 5 5
434 4 2 2 5 3 1 5 4 4 4.0 4 4 3
435 2 2 3 5 3 4 5 3 2 5.0 3 3 3
436 4 2 4 5 5 4 4 4 2 5.0 3 3 3
437 4 2 4 5 4 3 4 5 4 5.0 5 5 5
438 4 2 3 5 4 4 4 4 4 5.0 5 5 4
439 1 1 4 5 4 4 4 4 4 5.0 5 4 4
440 1 1 1 5 3 4 5 4 5 5.0 5 5 5
441 4 3 4 4 5 4 5 3 3 3.0 3 2 3
442 5 3 3 3 3 3 5 5 4 4.0 5 2 2
443 5 3 3 5 3 3 5 4 4 5.0 3 2 2
444 5 3 3 3 3 3 5 3 3 5.0 3 2 2
445 3 3 2 4 2 2 4 4 3 3.0 2 1 2
446 3 2 2 4 2 2 5 4 3 3.0 3 2 2
447 4 4 2 4 3 3 5 2 4 5.0 4 4 3
448 4 4 3 5 3 4 4 4 3 5.0 3 5 3
449 5 5 5 5 5 5 5 5 5 5.0 5 5 5
450 4 4 4 4 4 4 4 4 4 4.0 4 4 4
451 4 4 5 4 3 4 4 3 3 4.0 4 4 3
452 2 2 2 4 2 5 5 4 3 4.0 4 3 3
453 2 2 2 4 2 2 4 4 4 4.0 3 3 3
454 2 2 1 4 2 4 4 4 3 4.0 4 3 3
455 3 1 1 4 3 1 4 2 1 5.0 5 2 1
456 1 1 1 5 1 5 5 5 1 5.0 4 2 2
457 2 2 2 4 2 3 5 4 4 4.0 4 4 4
458 1 2 2 4 3 3 5 5 4 4.0 4 3 2
459 2 2 2 4 2 2 4 3 2 4.0 3 2 3
460 2 2 2 5 2 2 5 5 4 4.0 4 2 2
461 4 3 2 4 4 2 4 3 2 4.0 4 3 4
462 1 1 1 5 1 1 5 3 2 5.0 3 2 1
463 2 2 3 4 3 4 4 4 4 4.0 4 3 3
464 4 4 2 4 3 3 5 4 4 4.0 4 5 5
465 1 1 1 5 1 1 5 5 1 5.0 1 1 1
466 3 3 5 5 3 4 5 5 4 5.0 4 5 4
467 3 3 2 5 2 4 5 4 4 5.0 3 3 3
468 2 2 2 4 3 4 5 5 4 5.0 5 4 3
469 4 4 3 3 3 5 3 4 3 3.0 3 3 3
470 2 2 2 4 3 4 5 5 4 5.0 5 4 3
471 4 2 3 4 3 4 4 4 4 3.0 5 3 4
472 4 3 3 3 4 3 4 4 3 5.0 3 4 4
473 2 2 4 4 4 3 2 3 3 2.0 4 4 2
474 1 1 2 5 3 5 5 4 4 5.0 5 5 3
475 4 3 2 5 4 4 5 5 4 4.0 5 5 4
476 3 2 2 4 2 2 5 4 2 4.0 5 3 2
477 2 2 2 5 2 4 4 3 2 4.0 4 2 2
478 5 2 2 5 2 2 5 5 5 5.0 2 2 2
479 1 2 1 4 2 1 5 3 2 4.0 2 1 1
480 3 3 3 5 3 3 5 3 3 4.0 3 3 3
481 5 4 4 5 4 5 4 5 4 5.0 4 4 3
482 1 1 3 4 3 4 4 4 4 4.0 5 4 4
483 3 3 3 4 3 2 5 3 3 3.0 3 3 3
484 2 1 1 4 1 2 4 3 3 5.0 4 3 2
485 4 4 4 5 4 3 5 3 3 5.0 3 5 4
486 4 2 2 5 1 2 5 4 4 4.0 3 4 2
487 3 3 3 5 4 3 5 4 3 5.0 3 4 3
488 5 5 4 3 5 5 5 5 4 5.0 5 4 3
489 3 3 4 5 4 3 5 3 4 5.0 4 3 3
490 4 2 3 5 3 4 4 4 4 4.0 3 4 4
491 2 2 2 5 2 2 5 4 3 4.0 4 2 2
492 4 4 3 4 3 3 4 4 3 4.0 3 3 3
493 5 4 5 5 5 5 5 5 5 5.0 4 5 4
494 4 4 2 5 5 5 5 4 2 5.0 2 2 2
495 3 2 3 4 4 2 4 3 3 5.0 5 3 1
496 5 4 4 4 3 4 3 4 3 4.0 3 3 4
497 3 3 3 4 4 3 3 3 4 3.0 3 3 3
498 4 3 3 4 3 3 5 4 3 3.0 4 3 3
499 3 4 2 5 1 2 5 3 2 4.0 5 1 3
500 5 5 3 5 3 3 5 5 3 5.0 5 5 3
501 1 4 1 4 1 1 4 4 4 4.0 4 1 1
502 4 3 3 5 3 3 5 4 4 5.0 5 2 1
503 3 5 3 4 3 3 5 2 4 1.0 5 4 2
504 5 5 3 5 3 4 5 5 4 5.0 5 4 3
505 4 5 3 1 3 4 2 4 3 5.0 3 4 2
506 5 4 3 4 2 3 3 2 3 3.0 2 1 2
507 5 5 3 5 3 4 5 5 4 5.0 5 4 3
508 3 3 4 5 3 3 5 4 4 5.0 3 4 3
509 2 2 2 4 1 3 4 5 3 4.0 4 4 2
510 1 1 1 5 1 1 5 4 5 5.0 1 1 1
511 4 2 2 4 2 1 4 4 3 5.0 5 2 3
512 3 2 2 5 2 2 5 5 5 5.0 4 4 3
513 3 2 2 4 3 3 5 5 3 4.0 4 5 3
514 4 1 1 4 4 3 5 5 3 5.0 2 4 1
515 2 2 1 3 1 1 4 3 3 4.0 2 1 1
516 2 2 2 5 2 2 5 5 5 5.0 3 3 3
517 3 3 3 5 4 5 5 5 4 5.0 5 5 5
518 3 3 3 5 4 5 5 5 3 5.0 5 5 5
519 4 3 3 5 4 4 5 5 3 5.0 5 5 5
520 4 3 3 5 2 5 5 3 3 5.0 5 5 5
521 3 3 3 5 4 5 5 3 4 5.0 5 5 5
522 3 3 3 5 4 4 5 4 4 5.0 5 5 5
523 3 3 2 5 4 4 5 3 3 5.0 5 5 5
524 2 2 1 5 3 3 5 3 3 5.0 5 5 5
525 3 3 3 5 3 3 5 3 3 5.0 5 5 5
526 3 3 3 5 3 4 5 4 5 5.0 5 5 5
In [116]:
data_Usage_Factor['Call Friends and Family'] = data_Usage_Factor['Call Friends and Family'].astype(int)
data_Usage_Factor
Out[116]:
Feature All my friends use Old fashioned Prestige Secure Show-off Life style Communication Facilities Life easier Call Friends and Family Mostly use SMS Music or Videos Games
0 3 2 2 4 3 4 4 4 4 4 2 3 2
1 5 1 3 5 2 5 5 3 4 5 3 2 3
2 4 2 2 5 2 2 5 4 4 5 4 4 3
3 4 4 3 4 4 2 4 4 4 4 4 5 4
4 4 4 4 4 4 5 5 5 5 5 4 5 5
5 5 4 3 4 2 2 4 5 4 4 5 5 3
6 1 4 4 5 1 1 5 5 1 1 1 1 1
7 2 2 2 5 3 5 5 4 4 5 3 4 4
8 4 4 4 4 4 5 4 5 4 4 3 4 4
9 2 1 1 5 2 4 5 4 4 5 3 4 2
10 3 3 2 4 3 3 5 3 2 3 2 3 2
11 3 2 1 5 3 4 5 5 5 5 3 5 3
12 4 2 2 5 4 4 5 4 4 5 2 4 4
13 4 3 2 5 2 2 5 4 3 3 3 2 1
14 3 3 3 4 2 2 5 5 3 5 4 5 3
15 4 4 4 3 2 2 4 2 3 5 5 2 3
16 1 1 1 1 1 1 5 1 1 1 1 1 1
17 3 3 2 5 1 2 5 4 4 3 3 3 2
18 2 3 2 5 2 3 5 4 4 3 3 2 2
19 2 3 2 5 2 2 5 4 4 3 3 2 3
20 1 1 1 3 1 1 5 3 5 3 1 1 1
21 5 5 5 2 5 5 2 3 2 2 5 3 2
22 4 3 2 5 4 4 4 4 4 3 2 2 2
23 3 4 3 4 4 5 5 3 4 4 5 4 4
24 2 2 2 5 3 3 5 3 3 3 5 3 2
25 2 2 2 5 2 2 5 5 5 5 3 3 3
26 4 4 2 5 3 5 5 4 5 5 4 4 4
27 4 1 1 5 3 5 5 4 5 4 4 3 3
28 3 2 3 5 3 4 5 4 3 5 4 4 3
29 3 3 4 5 1 2 5 3 5 5 2 1 1
30 4 3 3 4 3 4 5 5 5 5 3 5 4
31 1 1 1 3 1 1 3 3 4 4 3 3 2
32 4 3 3 5 2 3 5 3 4 5 3 4 1
33 5 3 4 3 5 5 5 5 5 5 5 5 5
34 3 3 3 4 3 4 3 5 3 5 4 3 3
35 5 4 3 4 5 3 4 5 4 3 5 4 3
36 4 3 4 2 3 4 3 5 3 5 2 4 2
37 4 4 5 5 4 3 5 5 3 4 5 4 5
38 4 4 4 3 2 1 1 1 1 2 3 4 5
39 3 3 3 3 3 3 3 3 3 3 3 3 3
40 3 3 3 1 1 5 1 4 4 4 2 4 2
41 4 3 3 3 3 3 3 3 3 3 3 3 3
42 2 2 1 1 2 3 4 5 3 2 3 2 1
43 3 3 3 3 3 3 3 3 3 3 3 3 3
44 2 1 2 5 1 3 5 5 4 5 2 3 3
45 2 2 2 5 2 3 5 4 4 5 5 4 4
46 4 3 5 4 4 2 5 2 1 3 5 2 4
47 5 4 5 4 5 4 5 4 4 4 5 4 5
48 4 3 3 4 3 4 5 3 2 3 2 3 2
49 5 4 4 4 5 4 5 4 3 5 4 4 4
50 3 3 3 5 3 4 4 3 2 3 3 4 4
51 4 2 3 4 4 4 4 4 4 4 4 4 4
52 5 4 3 3 4 5 5 5 4 3 5 5 4
53 5 4 5 5 5 3 3 3 5 5 5 5 3
54 5 4 5 4 5 4 3 5 4 5 4 5 5
55 5 5 5 4 5 4 5 4 5 4 5 4 5
56 5 5 5 4 5 4 5 5 4 5 5 4 5
57 5 4 5 3 2 3 5 1 5 4 5 3 5
58 3 4 3 5 4 3 5 4 4 4 3 4 4
59 5 2 5 5 4 4 4 5 5 5 4 5 5
60 5 2 2 4 2 2 4 2 4 4 2 2 2
61 4 5 3 5 5 5 5 5 4 4 3 3 3
62 5 4 5 3 5 3 5 2 5 1 5 5 3
63 5 4 5 3 5 3 5 3 5 2 5 1 3
64 5 4 5 4 5 4 3 5 3 5 3 5 2
65 5 4 4 3 4 3 5 5 4 4 2 2 4
66 3 4 3 5 3 2 5 3 4 4 5 5 3
67 5 5 5 4 5 4 5 5 4 5 5 3 3
68 3 4 3 5 3 2 5 4 5 4 5 4 3
69 4 2 1 4 4 2 4 4 4 4 4 4 2
70 4 2 3 4 4 4 5 4 3 4 2 1 2
71 4 2 2 4 2 3 4 3 4 5 4 3 2
72 2 3 2 4 2 4 5 5 4 5 4 4 5
73 5 3 3 5 5 5 5 5 4 5 5 3 5
74 3 2 2 3 2 2 4 3 2 4 2 2 2
75 2 2 2 5 3 2 5 5 3 5 4 4 3
76 4 2 2 5 2 2 4 2 2 5 5 2 2
77 4 4 1 5 4 4 5 4 4 4 4 3 3
78 4 2 2 4 2 2 4 4 4 4 4 3 3
79 4 3 4 3 3 3 4 3 3 5 4 4 3
80 5 3 4 5 4 5 3 5 4 4 5 3 5
81 2 2 2 4 2 4 4 4 4 4 3 3 2
82 5 4 3 5 3 4 1 3 1 1 1 3 5
83 3 3 1 5 1 1 5 4 1 5 4 1 2
84 5 4 3 5 4 2 3 3 2 2 5 2 1
85 4 4 4 4 4 4 4 4 4 4 4 4 4
86 3 2 2 5 4 3 5 4 4 4 3 4 4
87 3 2 2 4 1 2 4 4 3 4 4 2 1
88 3 2 2 5 2 3 5 4 4 4 2 2 2
89 5 4 4 5 3 3 5 5 5 5 4 4 3
90 5 5 4 5 3 5 3 2 4 2 4 3 4
91 4 4 4 5 4 3 4 5 5 4 5 4 4
92 4 3 3 2 3 4 5 4 3 3 4 4 4
93 3 1 1 5 1 1 5 2 2 5 1 2 1
94 4 4 3 3 5 2 5 2 2 3 1 5 3
95 2 2 2 5 3 2 4 4 4 4 2 2 2
96 4 2 4 4 3 4 4 4 4 5 5 2 1
97 3 5 5 5 3 5 5 5 5 5 5 5 5
98 5 3 4 5 3 4 3 1 4 5 2 3 5
99 2 3 3 4 4 3 4 4 3 4 2 3 2
100 1 4 1 5 3 3 5 4 4 4 4 3 2
101 5 3 2 5 3 4 5 5 5 5 5 5 4
102 4 3 3 4 3 3 5 5 4 5 3 4 3
103 2 2 2 5 2 5 5 4 5 5 2 2 2
104 2 2 2 5 2 2 5 2 2 5 2 2 2
105 3 4 4 5 1 1 5 1 5 5 1 1 1
106 3 4 3 4 4 3 4 4 4 3 4 4 4
107 4 3 5 4 2 3 3 3 4 4 2 3 2
108 4 2 2 3 1 2 5 4 3 4 3 3 2
109 2 2 2 5 5 5 5 5 5 5 5 5 5
110 2 2 2 5 2 2 4 5 3 4 4 4 1
111 3 3 2 4 2 2 5 4 3 5 1 1 2
112 4 3 4 4 5 5 5 4 3 4 4 2 2
113 3 3 3 5 3 3 5 4 4 4 4 5 4
114 2 2 4 5 4 4 5 4 4 5 4 4 3
115 3 3 2 3 3 2 3 2 3 5 3 2 2
116 5 4 4 4 3 3 5 5 4 5 3 4 3
117 3 3 4 5 5 5 5 5 5 5 5 5 3
118 3 3 3 4 3 4 4 4 4 5 4 4 2
119 2 2 2 5 4 4 5 5 5 5 4 4 3
120 2 3 2 5 3 4 5 5 4 5 1 2 2
121 4 3 4 4 4 4 4 4 4 4 4 4 4
122 5 5 4 4 3 4 5 4 3 5 3 5 3
123 5 3 5 5 3 5 4 5 5 5 5 5 5
124 2 4 2 5 2 2 5 4 4 4 3 4 2
125 5 5 4 5 3 3 5 5 5 5 3 3 3
126 5 3 2 5 2 3 5 5 4 5 2 2 2
127 2 4 1 5 1 1 5 5 5 5 4 1 2
128 1 1 1 1 1 1 1 1 1 1 1 1 1
129 5 5 5 5 5 5 5 5 5 5 5 5 5
130 5 5 5 5 5 5 5 5 5 5 5 5 5
131 2 1 1 3 1 1 5 5 4 4 1 1 1
132 4 2 1 5 1 2 5 2 3 5 1 2 2
133 1 1 1 4 3 3 5 3 3 3 2 1 1
134 3 4 2 3 5 5 4 4 3 4 4 3 1
135 3 1 1 5 1 3 5 2 5 5 3 2 2
136 5 3 2 5 1 1 5 5 4 5 3 2 1
137 5 3 2 4 2 2 4 3 3 5 4 3 2
138 3 5 4 3 5 2 5 4 3 3 5 4 3
139 3 3 2 4 2 4 5 5 5 5 5 3 2
140 2 2 2 4 2 4 5 4 4 4 3 4 3
141 2 3 2 4 2 3 4 3 3 4 3 2 3
142 4 3 4 5 3 3 5 4 5 5 5 2 2
143 2 2 2 4 2 2 4 4 2 4 4 2 2
144 5 3 4 5 2 3 5 4 4 5 2 5 5
145 5 5 4 5 5 5 5 5 5 5 5 4 3
146 2 2 1 5 1 2 5 5 5 5 5 2 2
147 5 4 4 5 4 4 5 5 4 5 4 4 4
148 4 4 3 5 4 4 5 5 4 5 3 5 5
149 1 1 1 5 1 1 5 1 4 5 1 1 1
150 2 3 2 4 2 2 4 3 2 4 3 2 4
151 5 5 3 4 4 3 5 5 5 5 5 3 3
152 2 2 2 5 2 5 5 5 5 5 5 4 3
153 3 3 3 4 2 3 4 4 4 4 3 3 2
154 4 5 1 4 1 1 5 5 5 5 4 1 1
155 3 4 4 5 4 4 4 4 4 5 4 3 3
156 4 4 2 4 2 4 5 4 4 3 2 2 2
157 3 2 3 4 3 4 4 4 4 4 3 4 2
158 2 2 2 4 3 2 5 2 2 4 2 2 1
159 2 2 1 4 2 2 4 4 4 4 3 2 2
160 3 4 3 4 4 3 4 4 4 4 4 4 3
161 3 3 2 5 2 3 5 4 3 5 5 4 4
162 2 5 2 5 2 2 5 5 5 5 2 5 4
163 4 3 2 5 5 4 5 5 5 5 5 5 4
164 2 2 4 5 5 5 5 5 5 5 2 5 5
165 5 5 5 4 5 4 5 4 5 5 5 4 2
166 5 1 2 5 4 4 5 5 4 5 5 3 3
167 2 2 2 5 2 2 5 5 5 5 5 2 5
168 4 2 2 5 3 3 5 4 3 5 5 3 3
169 2 2 2 5 2 2 5 5 5 5 5 5 5
170 4 2 2 5 2 3 5 4 4 4 2 3 3
171 4 3 3 4 2 2 4 4 4 5 5 5 2
172 3 2 2 5 3 4 5 4 4 5 4 2 2
173 2 2 1 5 2 2 5 4 4 4 5 3 3
174 2 3 2 4 3 2 5 5 4 5 5 4 3
175 2 4 2 5 2 2 5 5 5 5 5 5 5
176 2 1 1 5 1 1 5 5 5 5 1 5 1
177 2 2 3 4 2 4 5 3 4 5 5 2 5
178 2 2 2 4 2 3 4 4 4 4 3 4 4
179 4 3 5 5 5 5 5 5 4 4 5 4 3
180 5 5 5 5 2 5 3 5 5 5 5 2 2
181 3 2 2 4 2 2 4 4 4 4 3 2 2
182 4 1 1 5 1 1 5 4 4 5 4 5 4
183 4 2 2 4 2 2 4 4 3 4 4 5 4
184 2 3 2 4 2 2 5 4 3 4 4 4 5
185 1 1 1 4 1 3 3 4 4 4 3 2 1
186 2 4 2 4 1 1 5 5 2 5 2 1 2
187 2 2 2 5 3 5 5 5 5 5 5 5 5
188 5 4 1 5 2 1 1 3 1 3 3 5 4
189 1 1 1 5 5 5 5 5 5 5 3 5 3
190 3 3 2 5 3 5 5 5 5 5 3 5 3
191 2 2 2 5 2 5 5 5 5 5 3 5 3
192 5 5 5 5 5 5 5 5 5 5 5 5 4
193 4 4 4 5 5 5 5 5 5 5 5 5 5
194 4 3 3 5 5 5 5 5 5 5 5 5 5
195 2 2 2 5 5 5 5 5 5 5 5 5 5
196 2 2 2 4 5 5 5 5 5 5 5 5 5
197 2 2 2 2 2 5 5 5 5 5 5 5 5
198 2 2 2 5 5 5 5 5 5 5 5 5 3
199 3 3 3 5 5 5 5 5 5 5 5 5 3
200 3 4 2 4 2 4 4 4 4 4 4 4 3
201 3 3 2 4 2 3 4 4 4 4 4 4 4
202 2 2 2 4 2 4 4 2 2 4 4 2 2
203 5 2 2 4 2 2 5 5 3 5 5 2 2
204 5 2 3 5 1 4 5 5 5 5 5 1 1
206 3 2 2 5 2 2 5 3 2 5 3 2 2
207 5 2 2 5 2 5 5 5 5 4 4 4 5
208 4 3 4 4 4 5 4 3 4 3 3 3 3
209 3 3 5 5 5 5 5 5 3 5 3 3 3
210 4 2 2 4 2 4 4 4 4 4 4 2 4
211 1 1 1 5 1 1 5 4 3 4 3 1 1
212 4 4 3 3 4 4 5 5 5 5 5 5 5
213 2 2 3 4 2 2 4 3 3 4 3 2 2
214 3 3 2 5 1 5 5 4 4 4 4 3 4
215 3 3 2 1 2 4 4 4 4 4 2 2 4
216 2 4 2 4 3 3 4 4 2 4 2 4 4
217 5 3 2 2 2 2 4 5 3 4 5 2 1
218 3 2 1 5 5 4 5 3 3 5 1 3 5
219 4 3 4 5 3 4 5 5 5 5 4 4 2
220 4 4 4 4 4 2 4 4 2 4 2 2 2
221 3 2 2 5 1 2 5 4 3 5 4 2 4
222 3 2 2 4 2 2 4 4 2 4 4 2 3
223 3 2 2 4 2 2 4 4 2 2 3 3 3
224 3 2 3 4 1 3 5 3 3 5 2 1 1
225 4 2 2 5 2 2 4 3 3 3 3 3 3
226 2 2 3 5 1 2 4 4 4 5 5 4 3
227 2 2 3 5 2 3 5 5 5 5 4 5 5
228 2 2 2 5 1 1 5 3 4 2 4 3 4
229 4 4 2 4 2 2 4 3 2 4 2 2 2
230 2 2 2 4 2 2 4 3 2 4 2 2 2
231 2 2 2 4 2 2 4 2 2 4 2 4 2
232 4 4 2 4 2 2 4 4 4 4 4 4 4
233 5 4 2 4 2 4 5 5 4 5 5 4 5
234 2 5 2 5 2 1 5 2 1 5 5 3 1
235 2 3 2 5 2 3 4 4 2 5 3 3 1
236 2 4 2 4 2 2 5 5 2 5 4 3 4
237 2 4 2 1 2 2 4 4 2 1 2 3 2
238 3 3 2 4 2 3 4 4 3 4 3 3 3
239 2 1 1 4 1 1 4 4 2 4 4 2 3
240 3 4 5 5 2 4 3 4 4 4 4 4 4
241 2 2 2 4 2 2 4 3 2 4 4 2 2
242 4 3 3 4 4 4 4 4 4 4 3 3 4
243 2 2 2 4 2 2 4 4 2 4 2 2 4
244 4 4 2 5 5 4 5 5 5 5 3 5 3
245 3 3 5 5 5 5 5 5 5 5 3 5 2
246 2 4 2 4 2 2 4 4 3 2 2 2 2
247 1 1 1 5 4 4 5 5 4 5 4 4 4
248 2 2 2 5 3 4 5 5 3 4 4 4 4
249 3 3 3 5 5 5 5 5 5 5 4 5 3
250 4 3 3 3 5 3 5 5 5 5 5 5 3
251 4 3 3 5 5 5 5 5 5 5 3 5 3
252 3 3 2 5 3 2 5 5 3 3 3 4 2
253 2 2 2 5 4 4 2 5 5 5 5 5 5
254 2 5 5 5 2 4 5 5 5 5 5 5 5
255 2 2 2 5 3 4 5 5 5 5 5 5 5
256 5 5 5 5 5 4 5 5 5 5 5 5 5
257 2 2 2 5 2 4 5 5 5 5 5 5 5
258 5 5 5 5 5 5 5 5 5 5 5 5 5
259 2 2 2 5 3 5 5 5 5 5 5 5 5
260 2 5 5 5 3 5 5 5 5 5 5 5 5
261 2 2 2 5 3 5 5 5 5 5 5 5 5
262 2 2 5 5 5 5 5 5 5 5 5 5 5
263 3 1 1 4 1 1 5 3 3 5 2 5 1
264 1 2 2 5 3 3 5 5 4 4 4 3 4
265 5 4 3 3 2 2 4 5 3 5 5 5 3
266 2 2 2 4 3 2 3 3 3 4 2 3 3
267 3 2 1 5 3 2 5 4 4 4 3 2 4
268 3 1 2 5 3 2 5 4 3 3 3 1 3
269 1 1 1 5 1 1 5 5 5 5 3 5 5
270 1 1 1 5 1 1 5 3 1 5 4 1 3
271 5 4 4 4 4 4 4 4 2 4 2 2 4
272 3 2 1 5 2 2 5 4 3 5 5 5 3
273 2 1 1 4 1 1 4 4 4 4 4 4 4
274 1 1 2 5 2 2 4 2 3 4 3 4 2
275 3 4 2 5 2 2 5 4 5 5 4 2 2
276 4 1 1 5 5 5 5 5 5 5 5 5 5
277 2 2 1 5 4 1 5 5 4 5 3 3 4
278 3 2 2 4 2 4 4 4 4 4 4 4 4
279 2 4 2 5 1 2 5 4 4 4 4 2 4
280 5 4 2 5 2 4 5 4 3 5 3 3 4
281 1 1 1 4 2 2 4 3 3 3 3 3 4
282 4 4 2 4 2 4 4 4 2 4 5 4 3
283 2 2 4 5 2 2 5 4 2 3 5 1 3
284 4 4 5 5 4 4 5 3 4 5 4 4 4
285 2 4 2 4 2 2 4 4 4 4 4 4 4
286 4 4 2 4 2 2 3 4 2 4 3 4 4
287 3 4 2 4 2 2 4 4 2 4 5 2 2
288 2 2 3 4 2 4 4 4 4 4 4 4 4
289 2 2 2 4 2 2 4 4 4 4 2 4 4
290 2 2 2 4 2 2 4 4 4 4 4 4 4
291 2 2 2 4 2 2 4 4 4 4 4 4 4
292 4 3 4 4 2 3 1 4 3 3 3 4 3
293 5 4 2 5 5 4 4 3 3 4 5 5 4
294 2 5 2 5 2 2 5 5 5 5 4 4 5
295 2 4 2 4 2 4 4 4 4 4 2 2 4
296 5 4 2 5 5 5 5 5 5 5 5 5 4
297 2 2 2 3 3 3 3 4 4 4 5 5 5
298 2 3 4 3 2 3 4 3 5 5 4 4 5
299 4 3 3 3 3 4 4 4 4 4 4 4 2
300 4 3 3 4 4 3 4 4 3 4 2 2 4
301 3 4 3 5 3 4 3 2 3 5 4 4 5
302 2 2 4 5 5 2 4 4 5 4 5 4 3
303 5 2 4 4 4 3 4 4 4 5 5 5 4
304 2 2 4 5 5 2 5 5 2 2 4 3 4
305 2 2 4 5 5 2 5 5 4 4 5 4 4
306 4 2 3 4 2 2 4 3 2 4 4 2 2
307 3 2 2 4 1 2 4 4 4 4 5 2 4
308 4 3 4 4 3 4 4 2 4 4 4 3 3
309 4 3 4 5 3 3 4 4 4 4 4 4 3
310 2 2 2 4 4 4 4 4 4 4 4 2 2
311 4 4 4 4 2 4 4 5 5 5 5 4 4
312 4 4 4 5 2 4 5 5 4 5 5 5 5
313 3 2 2 4 2 4 4 4 4 4 4 3 2
314 5 2 2 5 1 1 5 5 5 5 5 4 3
315 5 2 2 4 1 2 4 5 4 5 5 5 2
316 4 4 3 4 2 2 4 2 3 4 4 4 4
317 5 4 4 5 2 3 5 5 3 5 3 2 3
318 1 1 1 5 2 4 5 5 5 5 5 5 5
319 1 1 1 5 2 3 5 5 3 5 4 1 1
320 1 1 1 4 1 1 4 1 1 4 3 4 4
321 2 2 2 5 2 2 5 3 3 5 4 4 3
322 3 2 1 3 2 2 3 4 2 3 4 3 4
323 3 2 1 3 2 2 3 4 2 3 4 3 4
324 2 2 2 4 2 4 4 4 4 4 4 4 4
325 2 1 2 5 1 2 5 4 2 4 4 4 3
326 4 4 2 4 2 2 4 4 4 2 2 2 2
327 3 3 3 3 4 4 4 4 4 4 4 3 3
328 1 1 1 4 2 2 4 4 4 4 4 4 2
329 5 3 3 5 5 3 5 4 5 2 5 5 1
330 5 4 3 5 4 5 2 5 3 2 3 1 1
331 5 3 3 3 4 3 3 4 2 2 4 2 2
332 4 4 3 5 2 2 5 5 5 5 2 2 5
333 3 3 3 5 3 3 5 3 5 3 1 1 3
334 5 2 2 2 2 3 2 5 2 4 2 1 1
335 5 2 2 5 5 5 5 5 4 5 3 5 5
336 4 5 2 5 5 4 1 3 1 2 5 4 1
337 4 3 2 5 5 4 5 3 3 4 4 5 5
338 4 2 4 3 4 4 4 2 4 4 4 5 5
339 3 4 4 3 3 3 3 2 2 4 1 1 3
340 4 2 4 5 4 4 4 2 4 4 4 5 5
341 4 1 1 5 3 3 5 5 3 5 3 2 4
342 5 2 3 5 4 3 2 1 3 3 5 5 5
343 3 2 1 5 4 2 5 4 3 3 5 3 3
344 4 2 2 5 3 5 4 5 4 5 5 3 3
345 3 3 2 5 1 3 5 5 4 5 3 3 2
346 5 3 3 3 2 2 2 4 2 4 4 4 2
347 5 2 2 5 3 2 4 4 3 5 5 4 4
348 4 2 2 4 2 3 4 4 4 4 4 2 4
349 5 4 3 5 2 5 5 4 5 5 5 2 4
350 4 4 3 5 3 5 5 5 4 5 3 3 4
351 4 4 2 5 3 4 5 5 4 5 4 4 3
352 4 3 2 5 3 3 5 5 4 4 4 4 4
353 3 2 1 5 1 1 5 3 3 5 4 1 3
354 3 3 3 5 4 4 5 5 3 5 3 2 5
355 4 4 2 5 5 3 5 5 3 5 3 4 4
356 4 3 3 5 4 4 5 5 5 5 4 3 3
357 4 4 3 5 4 4 5 5 4 5 4 3 3
358 4 4 3 4 4 3 5 5 4 5 3 4 4
359 3 4 2 5 4 4 5 4 4 4 3 3 3
360 3 4 3 4 3 3 4 4 3 4 4 4 4
361 2 4 2 4 4 3 4 2 4 4 5 5 5
362 2 2 2 5 2 4 4 4 4 4 4 4 3
363 4 3 2 5 4 4 5 5 5 5 3 3 3
364 4 4 3 5 5 5 5 4 4 4 5 5 3
365 4 3 2 5 3 5 5 5 5 5 5 3 3
366 2 2 2 4 2 2 4 2 2 4 2 2 2
367 4 2 4 4 2 4 4 4 4 4 4 4 4
368 2 2 2 4 2 2 4 4 2 4 4 2 2
369 3 2 3 5 2 3 5 5 5 3 5 3 5
370 3 2 3 5 2 3 5 5 5 3 5 3 5
371 3 2 3 5 2 3 5 5 5 3 5 4 3
372 4 2 3 5 2 2 5 5 5 3 4 5 5
373 3 3 2 5 2 2 5 4 3 5 4 3 5
374 4 3 4 4 3 2 3 3 4 4 2 4 3
375 3 1 2 3 5 3 4 1 4 5 1 2 5
376 4 2 2 4 2 2 4 4 4 4 4 4 4
377 5 4 4 5 2 4 4 4 5 4 5 5 2
378 4 2 2 5 2 3 5 5 5 5 4 4 3
379 2 2 2 5 2 3 5 5 5 5 4 4 3
380 4 2 4 5 5 4 4 4 2 4 4 4 4
381 4 2 5 5 5 4 4 4 2 4 4 4 4
382 4 2 4 4 4 4 4 2 2 5 4 2 4
383 5 3 2 5 2 5 5 5 5 5 2 5 5
384 4 2 2 4 2 4 4 4 2 4 2 2 4
385 4 3 1 5 3 4 4 2 4 4 3 3 1
386 4 3 1 4 1 1 4 1 1 5 2 2 3
387 2 2 2 5 1 2 5 1 1 5 3 1 1
388 5 3 2 5 2 4 5 5 5 5 4 2 4
389 4 3 1 5 3 5 5 5 5 5 5 3 4
390 4 4 2 5 3 3 5 5 5 5 4 4 4
391 4 3 1 5 5 5 5 5 5 5 4 3 3
392 5 4 2 5 3 5 5 5 5 5 5 4 4
393 5 5 3 5 3 5 5 5 5 5 5 4 4
394 5 4 2 5 5 5 5 5 3 5 4 3 3
395 1 1 1 3 1 2 4 3 3 4 3 4 3
396 3 4 3 4 4 3 4 5 4 5 3 3 2
397 4 3 2 4 2 3 4 5 4 4 4 3 4
398 2 2 3 5 2 3 4 3 4 4 3 3 3
399 3 1 2 4 2 2 4 3 2 3 4 4 3
400 2 2 2 5 1 2 5 1 1 5 3 1 1
401 4 4 2 5 1 3 5 5 5 4 5 5 1
402 3 4 1 4 1 3 5 4 3 3 1 4 4
403 4 4 2 5 4 4 5 4 3 4 3 4 3
404 5 3 5 5 1 1 4 3 1 3 3 4 2
405 4 4 1 4 1 2 5 4 1 4 5 1 3
406 1 1 1 3 1 1 3 3 1 3 4 1 3
407 4 3 1 4 1 1 4 2 2 5 2 1 1
408 3 3 2 5 1 1 4 2 2 3 1 1 3
409 3 3 2 5 1 1 4 2 2 3 5 5 3
410 4 1 1 4 3 1 3 1 1 4 4 1 3
411 1 1 1 3 2 2 3 4 1 4 4 4 4
412 3 1 1 3 1 1 4 2 2 3 3 3 3
413 3 4 1 4 3 2 4 2 2 5 3 4 4
414 4 3 2 5 2 2 5 4 4 5 4 4 3
415 4 3 5 5 2 3 3 4 5 5 2 1 1
416 3 3 3 4 3 4 4 4 3 4 3 4 2
417 1 1 1 3 1 2 5 3 4 4 1 3 1
418 5 4 2 2 2 4 3 4 4 5 5 2 5
419 3 3 3 4 3 3 4 5 4 4 4 4 2
420 2 4 2 4 2 4 4 4 4 4 4 4 4
421 1 3 3 5 3 4 5 5 5 5 5 5 2
422 5 2 4 3 5 5 3 5 4 3 4 4 4
423 4 4 3 5 5 4 5 5 4 5 4 4 4
424 1 3 1 4 3 2 3 5 4 5 3 5 4
425 2 2 1 5 1 1 5 3 3 4 4 3 3
426 3 2 1 3 4 3 3 4 2 4 3 3 3
427 1 5 1 5 1 1 5 2 3 5 5 2 4
428 4 4 3 5 4 4 4 5 4 5 4 4 4
429 4 3 2 5 4 3 5 4 3 4 5 4 2
430 4 2 2 5 5 4 4 4 3 5 5 5 4
431 3 2 2 5 3 2 4 4 3 5 4 5 2
432 3 4 3 5 5 3 5 3 3 5 5 3 2
433 5 4 2 5 4 4 4 4 4 5 5 5 5
434 4 2 2 5 3 1 5 4 4 4 4 4 3
435 2 2 3 5 3 4 5 3 2 5 3 3 3
436 4 2 4 5 5 4 4 4 2 5 3 3 3
437 4 2 4 5 4 3 4 5 4 5 5 5 5
438 4 2 3 5 4 4 4 4 4 5 5 5 4
439 1 1 4 5 4 4 4 4 4 5 5 4 4
440 1 1 1 5 3 4 5 4 5 5 5 5 5
441 4 3 4 4 5 4 5 3 3 3 3 2 3
442 5 3 3 3 3 3 5 5 4 4 5 2 2
443 5 3 3 5 3 3 5 4 4 5 3 2 2
444 5 3 3 3 3 3 5 3 3 5 3 2 2
445 3 3 2 4 2 2 4 4 3 3 2 1 2
446 3 2 2 4 2 2 5 4 3 3 3 2 2
447 4 4 2 4 3 3 5 2 4 5 4 4 3
448 4 4 3 5 3 4 4 4 3 5 3 5 3
449 5 5 5 5 5 5 5 5 5 5 5 5 5
450 4 4 4 4 4 4 4 4 4 4 4 4 4
451 4 4 5 4 3 4 4 3 3 4 4 4 3
452 2 2 2 4 2 5 5 4 3 4 4 3 3
453 2 2 2 4 2 2 4 4 4 4 3 3 3
454 2 2 1 4 2 4 4 4 3 4 4 3 3
455 3 1 1 4 3 1 4 2 1 5 5 2 1
456 1 1 1 5 1 5 5 5 1 5 4 2 2
457 2 2 2 4 2 3 5 4 4 4 4 4 4
458 1 2 2 4 3 3 5 5 4 4 4 3 2
459 2 2 2 4 2 2 4 3 2 4 3 2 3
460 2 2 2 5 2 2 5 5 4 4 4 2 2
461 4 3 2 4 4 2 4 3 2 4 4 3 4
462 1 1 1 5 1 1 5 3 2 5 3 2 1
463 2 2 3 4 3 4 4 4 4 4 4 3 3
464 4 4 2 4 3 3 5 4 4 4 4 5 5
465 1 1 1 5 1 1 5 5 1 5 1 1 1
466 3 3 5 5 3 4 5 5 4 5 4 5 4
467 3 3 2 5 2 4 5 4 4 5 3 3 3
468 2 2 2 4 3 4 5 5 4 5 5 4 3
469 4 4 3 3 3 5 3 4 3 3 3 3 3
470 2 2 2 4 3 4 5 5 4 5 5 4 3
471 4 2 3 4 3 4 4 4 4 3 5 3 4
472 4 3 3 3 4 3 4 4 3 5 3 4 4
473 2 2 4 4 4 3 2 3 3 2 4 4 2
474 1 1 2 5 3 5 5 4 4 5 5 5 3
475 4 3 2 5 4 4 5 5 4 4 5 5 4
476 3 2 2 4 2 2 5 4 2 4 5 3 2
477 2 2 2 5 2 4 4 3 2 4 4 2 2
478 5 2 2 5 2 2 5 5 5 5 2 2 2
479 1 2 1 4 2 1 5 3 2 4 2 1 1
480 3 3 3 5 3 3 5 3 3 4 3 3 3
481 5 4 4 5 4 5 4 5 4 5 4 4 3
482 1 1 3 4 3 4 4 4 4 4 5 4 4
483 3 3 3 4 3 2 5 3 3 3 3 3 3
484 2 1 1 4 1 2 4 3 3 5 4 3 2
485 4 4 4 5 4 3 5 3 3 5 3 5 4
486 4 2 2 5 1 2 5 4 4 4 3 4 2
487 3 3 3 5 4 3 5 4 3 5 3 4 3
488 5 5 4 3 5 5 5 5 4 5 5 4 3
489 3 3 4 5 4 3 5 3 4 5 4 3 3
490 4 2 3 5 3 4 4 4 4 4 3 4 4
491 2 2 2 5 2 2 5 4 3 4 4 2 2
492 4 4 3 4 3 3 4 4 3 4 3 3 3
493 5 4 5 5 5 5 5 5 5 5 4 5 4
494 4 4 2 5 5 5 5 4 2 5 2 2 2
495 3 2 3 4 4 2 4 3 3 5 5 3 1
496 5 4 4 4 3 4 3 4 3 4 3 3 4
497 3 3 3 4 4 3 3 3 4 3 3 3 3
498 4 3 3 4 3 3 5 4 3 3 4 3 3
499 3 4 2 5 1 2 5 3 2 4 5 1 3
500 5 5 3 5 3 3 5 5 3 5 5 5 3
501 1 4 1 4 1 1 4 4 4 4 4 1 1
502 4 3 3 5 3 3 5 4 4 5 5 2 1
503 3 5 3 4 3 3 5 2 4 1 5 4 2
504 5 5 3 5 3 4 5 5 4 5 5 4 3
505 4 5 3 1 3 4 2 4 3 5 3 4 2
506 5 4 3 4 2 3 3 2 3 3 2 1 2
507 5 5 3 5 3 4 5 5 4 5 5 4 3
508 3 3 4 5 3 3 5 4 4 5 3 4 3
509 2 2 2 4 1 3 4 5 3 4 4 4 2
510 1 1 1 5 1 1 5 4 5 5 1 1 1
511 4 2 2 4 2 1 4 4 3 5 5 2 3
512 3 2 2 5 2 2 5 5 5 5 4 4 3
513 3 2 2 4 3 3 5 5 3 4 4 5 3
514 4 1 1 4 4 3 5 5 3 5 2 4 1
515 2 2 1 3 1 1 4 3 3 4 2 1 1
516 2 2 2 5 2 2 5 5 5 5 3 3 3
517 3 3 3 5 4 5 5 5 4 5 5 5 5
518 3 3 3 5 4 5 5 5 3 5 5 5 5
519 4 3 3 5 4 4 5 5 3 5 5 5 5
520 4 3 3 5 2 5 5 3 3 5 5 5 5
521 3 3 3 5 4 5 5 3 4 5 5 5 5
522 3 3 3 5 4 4 5 4 4 5 5 5 5
523 3 3 2 5 4 4 5 3 3 5 5 5 5
524 2 2 1 5 3 3 5 3 3 5 5 5 5
525 3 3 3 5 3 3 5 3 3 5 5 5 5
526 3 3 3 5 3 4 5 4 5 5 5 5 5
In [117]:
round(data_Usage_Factor.corr(),3).style.applymap(highlight_cell)
Out[117]:
Feature All my friends use Old fashioned Prestige Secure Show-off Life style Communication Facilities Life easier Call Friends and Family Mostly use SMS Music or Videos Games
Feature                          
All my friends use 1.000000 0.499000 0.473000 -0.001000 0.372000 0.303000 -0.090000 0.115000 0.126000 0.055000 0.176000 0.153000 0.148000
Old fashioned 0.499000 1.000000 0.498000 -0.002000 0.328000 0.261000 -0.013000 0.138000 0.135000 -0.001000 0.170000 0.176000 0.165000
Prestige 0.473000 0.498000 1.000000 0.018000 0.490000 0.406000 -0.079000 0.098000 0.209000 0.008000 0.217000 0.261000 0.247000
Secure -0.001000 -0.002000 0.018000 1.000000 0.099000 0.139000 0.436000 0.216000 0.265000 0.363000 0.180000 0.172000 0.155000
Show-off 0.372000 0.328000 0.490000 0.099000 1.000000 0.584000 0.052000 0.225000 0.220000 0.074000 0.275000 0.410000 0.329000
Life style 0.303000 0.261000 0.406000 0.139000 0.584000 1.000000 0.088000 0.370000 0.418000 0.228000 0.282000 0.398000 0.375000
Communication -0.090000 -0.013000 -0.079000 0.436000 0.052000 0.088000 1.000000 0.301000 0.343000 0.383000 0.132000 0.074000 0.082000
Facilities 0.115000 0.138000 0.098000 0.216000 0.225000 0.370000 0.301000 1.000000 0.497000 0.312000 0.284000 0.322000 0.210000
Life easier 0.126000 0.135000 0.209000 0.265000 0.220000 0.418000 0.343000 0.497000 1.000000 0.347000 0.279000 0.390000 0.290000
Call Friends and Family 0.055000 -0.001000 0.008000 0.363000 0.074000 0.228000 0.383000 0.312000 0.347000 1.000000 0.192000 0.227000 0.179000
Mostly use SMS 0.176000 0.170000 0.217000 0.180000 0.275000 0.282000 0.132000 0.284000 0.279000 0.192000 1.000000 0.430000 0.359000
Music or Videos 0.153000 0.176000 0.261000 0.172000 0.410000 0.398000 0.074000 0.322000 0.390000 0.227000 0.430000 1.000000 0.543000
Games 0.148000 0.165000 0.247000 0.155000 0.329000 0.375000 0.082000 0.210000 0.290000 0.179000 0.359000 0.543000 1.000000
In [118]:
import pandas as pd
import numpy as np
from scipy.stats import pearsonr

# Assuming 'df' is your DataFrame
# Create a correlation matrix
correlation_matrix = data_Usage_Factor.corr()

# Create an empty DataFrame for p-values
p_values_matrix = pd.DataFrame(np.zeros_like(correlation_matrix)
                               , columns=correlation_matrix.columns, index=correlation_matrix.index)

# Calculate p-values for each pair of variables
for i in range(len(correlation_matrix.columns)):
    for j in range(i+1, len(correlation_matrix.columns)):
        corr, p_value = pearsonr(data_Usage_Factor[correlation_matrix.columns[i]]
                                 , data_Usage_Factor[correlation_matrix.columns[j]])
        p_values_matrix.iloc[i, j] = p_value
        p_values_matrix.iloc[j, i] = p_value

# Print the correlation matrix
print("Correlation Matrix:")
display(correlation_matrix.style.applymap(highlight_cell))

# Print the p-values matrix
print("\nP-Values Matrix:")
display(round(p_values_matrix,3))
Correlation Matrix:
Feature All my friends use Old fashioned Prestige Secure Show-off Life style Communication Facilities Life easier Call Friends and Family Mostly use SMS Music or Videos Games
Feature                          
All my friends use 1.000000 0.498746 0.472517 -0.001349 0.371677 0.303350 -0.089704 0.114547 0.126377 0.055044 0.175527 0.152900 0.147724
Old fashioned 0.498746 1.000000 0.498092 -0.001736 0.327575 0.260526 -0.012605 0.138282 0.135351 -0.000598 0.170099 0.175803 0.164977
Prestige 0.472517 0.498092 1.000000 0.017706 0.489601 0.405839 -0.078796 0.098400 0.209075 0.007512 0.217011 0.260811 0.246758
Secure -0.001349 -0.001736 0.017706 1.000000 0.099391 0.138603 0.435566 0.216036 0.265256 0.362848 0.179875 0.172270 0.154889
Show-off 0.371677 0.327575 0.489601 0.099391 1.000000 0.583700 0.051842 0.225049 0.220372 0.074165 0.274754 0.409534 0.329304
Life style 0.303350 0.260526 0.405839 0.138603 0.583700 1.000000 0.087973 0.369733 0.417797 0.228042 0.282142 0.398226 0.374845
Communication -0.089704 -0.012605 -0.078796 0.435566 0.051842 0.087973 1.000000 0.301058 0.342671 0.382600 0.132334 0.074264 0.081835
Facilities 0.114547 0.138282 0.098400 0.216036 0.225049 0.369733 0.301058 1.000000 0.496751 0.311780 0.283562 0.321743 0.210002
Life easier 0.126377 0.135351 0.209075 0.265256 0.220372 0.417797 0.342671 0.496751 1.000000 0.346972 0.279440 0.389631 0.289965
Call Friends and Family 0.055044 -0.000598 0.007512 0.362848 0.074165 0.228042 0.382600 0.311780 0.346972 1.000000 0.192116 0.227030 0.178823
Mostly use SMS 0.175527 0.170099 0.217011 0.179875 0.274754 0.282142 0.132334 0.283562 0.279440 0.192116 1.000000 0.430105 0.359471
Music or Videos 0.152900 0.175803 0.260811 0.172270 0.409534 0.398226 0.074264 0.321743 0.389631 0.227030 0.430105 1.000000 0.543107
Games 0.147724 0.164977 0.246758 0.154889 0.329304 0.374845 0.081835 0.210002 0.289965 0.178823 0.359471 0.543107 1.000000
P-Values Matrix:
Feature All my friends use Old fashioned Prestige Secure Show-off Life style Communication Facilities Life easier Call Friends and Family Mostly use SMS Music or Videos Games
Feature
All my friends use 0.000 0.000 0.000 0.975 0.000 0.000 0.040 0.009 0.004 0.208 0.000 0.000 0.001
Old fashioned 0.000 0.000 0.000 0.968 0.000 0.000 0.773 0.001 0.002 0.989 0.000 0.000 0.000
Prestige 0.000 0.000 0.000 0.685 0.000 0.000 0.071 0.024 0.000 0.864 0.000 0.000 0.000
Secure 0.975 0.968 0.685 0.000 0.023 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Show-off 0.000 0.000 0.000 0.023 0.000 0.000 0.235 0.000 0.000 0.089 0.000 0.000 0.000
Life style 0.000 0.000 0.000 0.001 0.000 0.000 0.044 0.000 0.000 0.000 0.000 0.000 0.000
Communication 0.040 0.773 0.071 0.000 0.235 0.044 0.000 0.000 0.000 0.000 0.002 0.089 0.061
Facilities 0.009 0.001 0.024 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Life easier 0.004 0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Call Friends and Family 0.208 0.989 0.864 0.000 0.089 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Mostly use SMS 0.000 0.000 0.000 0.000 0.000 0.000 0.002 0.000 0.000 0.000 0.000 0.000 0.000
Music or Videos 0.000 0.000 0.000 0.000 0.000 0.000 0.089 0.000 0.000 0.000 0.000 0.000 0.000
Games 0.001 0.000 0.000 0.000 0.000 0.000 0.061 0.000 0.000 0.000 0.000 0.000 0.000
In [119]:
import pandas as pd
import numpy as np
from factor_analyzer import FactorAnalyzer
from factor_analyzer.factor_analyzer import calculate_kmo
from factor_analyzer.factor_analyzer import calculate_bartlett_sphericity

# Assuming 'data_HS_Factor' is your DataFrame
# Specify the number of factors
n_factors = 10

# Initialize FactorAnalyzer
fa = FactorAnalyzer(n_factors, rotation=None)

# Fit the model
fa.fit(data_Usage_Factor)

# Obtain the factor loadings
factor_loadings = pd.DataFrame(fa.loadings_, index=data_Usage_Factor.columns)

# Calculate Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy

kmo_per_variable, kmo_total = calculate_kmo(data_Usage_Factor)

# Print the KMO values
print("Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy per variable:")
print(kmo_per_variable)
print("\nKaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy (Total):", kmo_total)

chi2_statistic,p_value = calculate_bartlett_sphericity(data_Usage_Factor)

# Print the test statistics
print("\nBartlett's Test of Sphericity:")
print(f"Chi-Square Statistic: {chi2_statistic}")
print(f"P-value: {p_value}")
Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy per variable:
[0.80430188 0.78637892 0.82539714 0.79300566 0.80462342 0.83527949
 0.69673044 0.84693816 0.82979813 0.82782272 0.90534089 0.8153543
 0.84438626]

Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy (Total): 0.8188382947514252

Bartlett's Test of Sphericity:
Chi-Square Statistic: 1950.3964695305046
P-value: 0.0
In [120]:
from scipy.cluster.hierarchy import linkage, dendrogram

# Assuming 'data_HS_Factor' is your data matrix
# Transpose the data matrix to cluster on columns
data_transposed = data_Usage_Factor.T

# Perform hierarchical clustering with single linkage
linkage_matrix = linkage(data_transposed, method='single')

# Create a dendrogram
#dendrogram(linkage_matrix, orientation='top', labels=data_HS_Factor.columns, distance_sort='descending', show_leaf_counts=True)

# Create a dendrogram with transposed orientation
dendrogram(linkage_matrix, orientation='right', labels=data_Usage_Factor.columns
           , distance_sort='descending', show_leaf_counts=True)

# Add labels and title
plt.xlabel('Feature Index')
plt.ylabel('Distance')
plt.title('Dendrogram with Single Linkage on Columns')

# Show the plot
plt.show()
In [121]:
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans

# Assuming 'data_HS_Factor' is your data matrix
# Transpose the data matrix to cluster on columns
data_transposed = data_Usage_Factor.T

# Specify the number of clusters (k)
num_clusters = 3  # You can adjust this based on your requirements

# Perform k-means clustering
kmeans = KMeans(n_clusters=num_clusters, random_state=42)
cluster_labels = kmeans.fit_predict(data_transposed)

# Visualize the results
plt.figure(figsize=(6, 4))
plt.scatter(data_transposed.index, cluster_labels, c=cluster_labels, cmap='viridis', marker='o', s=50)
plt.title('K-Means Clustering on Columns')
plt.xticks(rotation=90)
plt.xlabel('Feature Index')
plt.ylabel('Cluster Label')
plt.show()
C:\Users\vkakarla\AppData\Local\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
In [122]:
import matplotlib.pyplot as plt
import numpy as np
from factor_analyzer import FactorAnalyzer

# Assuming 'data_HS_Factor' is your DataFrame
# Specify a range of factor numbers
num_factors_range = range(1, len(data_Usage_Factor.columns) + 1)

# Create an empty list to store eigenvalues
eigenvalues = []

# Iterate over different numbers of factors
for n_factors in num_factors_range:
    fa = FactorAnalyzer(n_factors, method='principal', rotation='varimax')
    fa.fit(data_Usage_Factor)
    eigenvalues.append(fa.get_eigenvalues()[0][n_factors - 1])  # Extract the eigenvalue for the specified number of factors

# Plot the scree plot
plt.plot(num_factors_range, eigenvalues, marker='o')
plt.title('Scree Plot')
plt.xlabel('Number of Factors')
plt.ylabel('Eigenvalues')
plt.show()
C:\Users\vkakarla\AppData\Local\anaconda3\lib\site-packages\factor_analyzer\factor_analyzer.py:663: UserWarning: No rotation will be performed when the number of factors equals 1.
  warnings.warn(
In [123]:
import pandas as pd
from factor_analyzer import FactorAnalyzer

# Assuming 'data_HS_Factor' is your DataFrame
# Specify the number of factors
n_factors = 3

#The rotation must be one of the following: 
#['varimax', 'oblimax', 'quartimax', 'equamax', 'geomin_ort', 'promax', 'oblimin', 'quartimin', 'geomin_obl', None]
# Defaults to ‘promax’.

# Create a FactorAnalyzer object
fa_woer = FactorAnalyzer(len(data_Usage_Factor.columns),method='principal',rotation=None) #without extracation and rotation
fa_wer  = FactorAnalyzer(n_factors,method='principal', rotation='varimax')
 
# Fit the factor model to your data
fa_woer.fit(data_Usage_Factor)
fa_wer.fit(data_Usage_Factor)

# Get the variable names
features = data_Usage_Factor.columns

# Get initial communalities
communalities_without_extraction_rotation = fa_woer.get_communalities()

# Get extracted communalities
communalities_with_extraction_rotation = fa_wer.get_communalities()

# Create a DataFrame with feature, initial communality, and extracted communality
communalities_table = pd.DataFrame({    'Feature': features,    
                                    'Communalities (Initial)': communalities_without_extraction_rotation,
                                    'Communalities (With Extraction and Rotation)': communalities_with_extraction_rotation,
                                   })

# Display the communalities table
# print('\n',features)
# print('\n',communalities_without_rotation)
# print('\n',communalities_with_rotation,'\n')

communalities_table.style.applymap(highlight_cell)
Out[123]:
  Feature Communalities (Initial) Communalities (With Extraction and Rotation)
0 All my friends use 1.000000 0.633249
1 Old fashioned 1.000000 0.619626
2 Prestige 1.000000 0.645572
3 Secure 1.000000 0.477266
4 Show-off 1.000000 0.556583
5 Life style 1.000000 0.547260
6 Communication 1.000000 0.636375
7 Facilities 1.000000 0.443585
8 Life easier 1.000000 0.525172
9 Call Friends and Family 1.000000 0.500557
10 Mostly use SMS 1.000000 0.419769
11 Music or Videos 1.000000 0.693950
12 Games 1.000000 0.605794
In [124]:
import pandas as pd
from factor_analyzer import FactorAnalyzer

# Assuming 'data_HS_Factor' is your DataFrame
# Specify the number of factors
n_factors = 3

# Create a FactorAnalyzer object
fa = FactorAnalyzer(n_factors, method='principal', rotation='varimax')

# Fit the factor model to your data
fa.fit(data_Usage_Factor)

# Get the explained variance
explained_variance = fa.get_factor_variance()

# Get additional information
initial_eigenvalues = fa.get_eigenvalues()[0]
percentage_of_variance = initial_eigenvalues / sum(initial_eigenvalues) * 100
cumulative_percentage_of_variance = percentage_of_variance.cumsum()

rotation_sums_squared_loadings = fa.get_factor_variance()[0]
percentage_of_rotation_sums_squared_loadings = fa.get_factor_variance()[1]*100
cumulative_percentage_of_rotation_sums_squared_loadings = fa.get_factor_variance()[2]*100

intial_variance_table = pd.DataFrame({
    'Component': [f'{i + 1}' for i in range(len(data_Usage_Factor.columns))],
    'Initial Eigenvalues': initial_eigenvalues,
    'Initial Eigenvalues (% of Variance)': percentage_of_variance,
    'Initial Eigenvalues (%Cumulative of Variance)': cumulative_percentage_of_variance,

})

factor_variance_table = pd.DataFrame({
    'Component': [f'{i + 1}' for i in range(n_factors)],
    'Rotation SS Loadings': rotation_sums_squared_loadings,
    'Rotation SS Loadings (% of Variance)': percentage_of_rotation_sums_squared_loadings,
    'Rotation SS Loadings (Cumulative of Variance)': cumulative_percentage_of_rotation_sums_squared_loadings
})

# Combine both dataframes
combined_table = pd.merge(intial_variance_table, factor_variance_table, on='Component', how='left')

# Display the combined table
print("Total Variance Explained Table:")
round(combined_table,3)
Total Variance Explained Table:
Out[124]:
Component Initial Eigenvalues Initial Eigenvalues (% of Variance) Initial Eigenvalues (%Cumulative of Variance) Rotation SS Loadings Rotation SS Loadings (% of Variance) Rotation SS Loadings (Cumulative of Variance)
0 1 4.005 30.805 30.805 2.521 19.395 19.395
1 2 2.133 16.405 47.210 2.336 17.971 37.366
2 3 1.167 8.981 56.190 2.447 18.825 56.190
3 4 0.871 6.701 62.891 NaN NaN NaN
4 5 0.785 6.040 68.932 NaN NaN NaN
5 6 0.673 5.173 74.105 NaN NaN NaN
6 7 0.635 4.883 78.988 NaN NaN NaN
7 8 0.545 4.189 83.178 NaN NaN NaN
8 9 0.534 4.107 87.285 NaN NaN NaN
9 10 0.479 3.684 90.969 NaN NaN NaN
10 11 0.450 3.459 94.428 NaN NaN NaN
11 12 0.412 3.168 97.596 NaN NaN NaN
12 13 0.313 2.404 100.000 NaN NaN NaN
In [125]:
import pandas as pd
from factor_analyzer import FactorAnalyzer

# Assuming 'data_HS_Factor' is your DataFrame
# Specify the number of factors
n_factors = 3

# Create a FactorAnalyzer object
fa = FactorAnalyzer(n_factors,method='principal', rotation=None)

# Fit the factor model to your data
fa.fit(data_Usage_Factor)

# Get the component matrix
component_matrix = pd.DataFrame(fa.loadings_, index=data_Usage_Factor.columns
                                , columns=[f'Factor {i + 1}' for i in range(n_factors)])

# Display the component matrix
print("Component Matrix:")
component_matrix.style.applymap(highlight_cell)
Component Matrix:
Out[125]:
  Factor 1 Factor 2 Factor 3
Feature      
All my friends use 0.467659 -0.515935 0.385169
Old fashioned 0.466294 -0.492869 0.399093
Prestige 0.566510 -0.533434 0.200217
Secure 0.353252 0.530536 0.266479
Show-off 0.669946 -0.326937 -0.029458
Life style 0.728591 -0.120073 -0.044685
Communication 0.294663 0.646400 0.362927
Facilities 0.570056 0.336049 0.075446
Life easier 0.637243 0.340172 0.058111
Call Friends and Family 0.418323 0.532059 0.206095
Mostly use SMS 0.567913 0.070017 -0.303878
Music or Videos 0.681252 0.041663 -0.477609
Games 0.603001 0.004764 -0.492100
In [126]:
import pandas as pd
from factor_analyzer import FactorAnalyzer

# Assuming 'data_HS_Factor' is your DataFrame
# Specify the number of factors
n_factors = 3

# Create a FactorAnalyzer object
fa = FactorAnalyzer(n_factors, method='principal', rotation='varimax')

# Fit the factor model to your data
fa.fit(data_Usage_Factor)

# Get the component matrix
component_matrix = pd.DataFrame(fa.loadings_, index=data_Usage_Factor.columns
                                , columns=[f'Factor {i + 1}' for i in range(n_factors)])

# Display the component matrix
print("Rotated Component Matrix:")
component_matrix.style.applymap(highlight_cell)
Rotated Component Matrix:
Out[126]:
  Factor 1 Factor 2 Factor 3
Feature      
All my friends use 0.037781 0.005005 0.794856
Old fashioned 0.027074 0.028202 0.786192
Prestige 0.239547 -0.047079 0.765489
Secure 0.058331 0.688225 -0.014505
Show-off 0.479761 0.051766 0.568975
Life style 0.534001 0.228504 0.458136
Communication -0.050344 0.792793 -0.072939
Facilities 0.343642 0.554561 0.134003
Life easier 0.402638 0.581050 0.159486
Call Friends and Family 0.146955 0.691987 -0.010705
Mostly use SMS 0.612902 0.178208 0.111182
Music or Videos 0.816421 0.129829 0.102714
Games 0.772360 0.058783 0.076146
In [ ]:
 

After going thru the above analysis we decide to keep only the below variables and run the Factor analysis again¶

In [127]:
Usage_Columns_Final = ['All my friends use',
                'Old fashioned',
                'Prestige',
                'Secure',
                'Communication',
                'Call Friends and Family',
                'Mostly use SMS',
                'Music or Videos',
                'Games'
               ]

data_Usage_Factor_Final = data2[Usage_Columns_Final].dropna()
data_Usage_Factor_Final
Out[127]:
Feature All my friends use Old fashioned Prestige Secure Communication Call Friends and Family Mostly use SMS Music or Videos Games
0 3 2 2 4 4 4.0 2 3 2
1 5 1 3 5 5 5.0 3 2 3
2 4 2 2 5 5 5.0 4 4 3
3 4 4 3 4 4 4.0 4 5 4
4 4 4 4 4 5 5.0 4 5 5
5 5 4 3 4 4 4.0 5 5 3
6 1 4 4 5 5 1.0 1 1 1
7 2 2 2 5 5 5.0 3 4 4
8 4 4 4 4 4 4.0 3 4 4
9 2 1 1 5 5 5.0 3 4 2
10 3 3 2 4 5 3.0 2 3 2
11 3 2 1 5 5 5.0 3 5 3
12 4 2 2 5 5 5.0 2 4 4
13 4 3 2 5 5 3.0 3 2 1
14 3 3 3 4 5 5.0 4 5 3
15 4 4 4 3 4 5.0 5 2 3
16 1 1 1 1 5 1.0 1 1 1
17 3 3 2 5 5 3.0 3 3 2
18 2 3 2 5 5 3.0 3 2 2
19 2 3 2 5 5 3.0 3 2 3
20 1 1 1 3 5 3.0 1 1 1
21 5 5 5 2 2 2.0 5 3 2
22 4 3 2 5 4 3.0 2 2 2
23 3 4 3 4 5 4.0 5 4 4
24 2 2 2 5 5 3.0 5 3 2
25 2 2 2 5 5 5.0 3 3 3
26 4 4 2 5 5 5.0 4 4 4
27 4 1 1 5 5 4.0 4 3 3
28 3 2 3 5 5 5.0 4 4 3
29 3 3 4 5 5 5.0 2 1 1
30 4 3 3 4 5 5.0 3 5 4
31 1 1 1 3 3 4.0 3 3 2
32 4 3 3 5 5 5.0 3 4 1
33 5 3 4 3 5 5.0 5 5 5
34 3 3 3 4 3 5.0 4 3 3
35 5 4 3 4 4 3.0 5 4 3
36 4 3 4 2 3 5.0 2 4 2
37 4 4 5 5 5 4.0 5 4 5
38 4 4 4 3 1 2.0 3 4 5
39 3 3 3 3 3 3.0 3 3 3
40 3 3 3 1 1 4.0 2 4 2
41 4 3 3 3 3 3.0 3 3 3
42 2 2 1 1 4 2.0 3 2 1
43 3 3 3 3 3 3.0 3 3 3
44 2 1 2 5 5 5.0 2 3 3
45 2 2 2 5 5 5.0 5 4 4
46 4 3 5 4 5 3.0 5 2 4
47 5 4 5 4 5 4.0 5 4 5
48 4 3 3 4 5 3.0 2 3 2
49 5 4 4 4 5 5.0 4 4 4
50 3 3 3 5 4 3.0 3 4 4
51 4 2 3 4 4 4.0 4 4 4
52 5 4 3 3 5 3.0 5 5 4
53 5 4 5 5 3 5.0 5 5 3
54 5 4 5 4 3 5.0 4 5 5
55 5 5 5 4 5 4.0 5 4 5
56 5 5 5 4 5 5.0 5 4 5
57 5 4 5 3 5 4.0 5 3 5
58 3 4 3 5 5 4.0 3 4 4
59 5 2 5 5 4 5.0 4 5 5
60 5 2 2 4 4 4.0 2 2 2
61 4 5 3 5 5 4.0 3 3 3
62 5 4 5 3 5 1.0 5 5 3
63 5 4 5 3 5 2.0 5 1 3
64 5 4 5 4 3 5.0 3 5 2
65 5 4 4 3 5 4.0 2 2 4
66 3 4 3 5 5 4.0 5 5 3
67 5 5 5 4 5 5.0 5 3 3
68 3 4 3 5 5 4.0 5 4 3
69 4 2 1 4 4 4.0 4 4 2
70 4 2 3 4 5 4.0 2 1 2
71 4 2 2 4 4 5.0 4 3 2
72 2 3 2 4 5 5.0 4 4 5
73 5 3 3 5 5 5.0 5 3 5
74 3 2 2 3 4 4.0 2 2 2
75 2 2 2 5 5 5.0 4 4 3
76 4 2 2 5 4 5.0 5 2 2
77 4 4 1 5 5 4.0 4 3 3
78 4 2 2 4 4 4.0 4 3 3
79 4 3 4 3 4 5.0 4 4 3
80 5 3 4 5 3 4.0 5 3 5
81 2 2 2 4 4 4.0 3 3 2
82 5 4 3 5 1 1.0 1 3 5
83 3 3 1 5 5 5.0 4 1 2
84 5 4 3 5 3 2.0 5 2 1
85 4 4 4 4 4 4.0 4 4 4
86 3 2 2 5 5 4.0 3 4 4
87 3 2 2 4 4 4.0 4 2 1
88 3 2 2 5 5 4.0 2 2 2
89 5 4 4 5 5 5.0 4 4 3
90 5 5 4 5 3 2.0 4 3 4
91 4 4 4 5 4 4.0 5 4 4
92 4 3 3 2 5 3.0 4 4 4
93 3 1 1 5 5 5.0 1 2 1
94 4 4 3 3 5 3.0 1 5 3
95 2 2 2 5 4 4.0 2 2 2
96 4 2 4 4 4 5.0 5 2 1
97 3 5 5 5 5 5.0 5 5 5
98 5 3 4 5 3 5.0 2 3 5
99 2 3 3 4 4 4.0 2 3 2
100 1 4 1 5 5 4.0 4 3 2
101 5 3 2 5 5 5.0 5 5 4
102 4 3 3 4 5 5.0 3 4 3
103 2 2 2 5 5 5.0 2 2 2
104 2 2 2 5 5 5.0 2 2 2
105 3 4 4 5 5 5.0 1 1 1
106 3 4 3 4 4 3.0 4 4 4
107 4 3 5 4 3 4.0 2 3 2
108 4 2 2 3 5 4.0 3 3 2
109 2 2 2 5 5 5.0 5 5 5
110 2 2 2 5 4 4.0 4 4 1
111 3 3 2 4 5 5.0 1 1 2
112 4 3 4 4 5 4.0 4 2 2
113 3 3 3 5 5 4.0 4 5 4
114 2 2 4 5 5 5.0 4 4 3
115 3 3 2 3 3 5.0 3 2 2
116 5 4 4 4 5 5.0 3 4 3
117 3 3 4 5 5 5.0 5 5 3
118 3 3 3 4 4 5.0 4 4 2
119 2 2 2 5 5 5.0 4 4 3
120 2 3 2 5 5 5.0 1 2 2
121 4 3 4 4 4 4.0 4 4 4
122 5 5 4 4 5 5.0 3 5 3
123 5 3 5 5 4 5.0 5 5 5
124 2 4 2 5 5 4.0 3 4 2
125 5 5 4 5 5 5.0 3 3 3
126 5 3 2 5 5 5.0 2 2 2
127 2 4 1 5 5 5.0 4 1 2
128 1 1 1 1 1 1.0 1 1 1
129 5 5 5 5 5 5.0 5 5 5
130 5 5 5 5 5 5.0 5 5 5
131 2 1 1 3 5 4.0 1 1 1
132 4 2 1 5 5 5.0 1 2 2
133 1 1 1 4 5 3.0 2 1 1
134 3 4 2 3 4 4.0 4 3 1
135 3 1 1 5 5 5.0 3 2 2
136 5 3 2 5 5 5.0 3 2 1
137 5 3 2 4 4 5.0 4 3 2
138 3 5 4 3 5 3.0 5 4 3
139 3 3 2 4 5 5.0 5 3 2
140 2 2 2 4 5 4.0 3 4 3
141 2 3 2 4 4 4.0 3 2 3
142 4 3 4 5 5 5.0 5 2 2
143 2 2 2 4 4 4.0 4 2 2
144 5 3 4 5 5 5.0 2 5 5
145 5 5 4 5 5 5.0 5 4 3
146 2 2 1 5 5 5.0 5 2 2
147 5 4 4 5 5 5.0 4 4 4
148 4 4 3 5 5 5.0 3 5 5
149 1 1 1 5 5 5.0 1 1 1
150 2 3 2 4 4 4.0 3 2 4
151 5 5 3 4 5 5.0 5 3 3
152 2 2 2 5 5 5.0 5 4 3
153 3 3 3 4 4 4.0 3 3 2
154 4 5 1 4 5 5.0 4 1 1
155 3 4 4 5 4 5.0 4 3 3
156 4 4 2 4 5 3.0 2 2 2
157 3 2 3 4 4 4.0 3 4 2
158 2 2 2 4 5 4.0 2 2 1
159 2 2 1 4 4 4.0 3 2 2
160 3 4 3 4 4 4.0 4 4 3
161 3 3 2 5 5 5.0 5 4 4
162 2 5 2 5 5 5.0 2 5 4
163 4 3 2 5 5 5.0 5 5 4
164 2 2 4 5 5 5.0 2 5 5
165 5 5 5 4 5 5.0 5 4 2
166 5 1 2 5 5 5.0 5 3 3
167 2 2 2 5 5 5.0 5 2 5
168 4 2 2 5 5 5.0 5 3 3
169 2 2 2 5 5 5.0 5 5 5
170 4 2 2 5 5 4.0 2 3 3
171 4 3 3 4 4 5.0 5 5 2
172 3 2 2 5 5 5.0 4 2 2
173 2 2 1 5 5 4.0 5 3 3
174 2 3 2 4 5 5.0 5 4 3
175 2 4 2 5 5 5.0 5 5 5
176 2 1 1 5 5 5.0 1 5 1
177 2 2 3 4 5 5.0 5 2 5
178 2 2 2 4 4 4.0 3 4 4
179 4 3 5 5 5 4.0 5 4 3
180 5 5 5 5 3 5.0 5 2 2
181 3 2 2 4 4 4.0 3 2 2
182 4 1 1 5 5 5.0 4 5 4
183 4 2 2 4 4 4.0 4 5 4
184 2 3 2 4 5 4.0 4 4 5
185 1 1 1 4 3 4.0 3 2 1
186 2 4 2 4 5 5.0 2 1 2
187 2 2 2 5 5 5.0 5 5 5
188 5 4 1 5 1 3.0 3 5 4
189 1 1 1 5 5 5.0 3 5 3
190 3 3 2 5 5 5.0 3 5 3
191 2 2 2 5 5 5.0 3 5 3
192 5 5 5 5 5 5.0 5 5 4
193 4 4 4 5 5 5.0 5 5 5
194 4 3 3 5 5 5.0 5 5 5
195 2 2 2 5 5 5.0 5 5 5
196 2 2 2 4 5 5.0 5 5 5
197 2 2 2 2 5 5.0 5 5 5
198 2 2 2 5 5 5.0 5 5 3
199 3 3 3 5 5 5.0 5 5 3
200 3 4 2 4 4 4.0 4 4 3
201 3 3 2 4 4 4.0 4 4 4
202 2 2 2 4 4 4.0 4 2 2
203 5 2 2 4 5 5.0 5 2 2
204 5 2 3 5 5 5.0 5 1 1
206 3 2 2 5 5 5.0 3 2 2
207 5 2 2 5 5 4.0 4 4 5
208 4 3 4 4 4 3.0 3 3 3
209 3 3 5 5 5 5.0 3 3 3
210 4 2 2 4 4 4.0 4 2 4
211 1 1 1 5 5 4.0 3 1 1
212 4 4 3 3 5 5.0 5 5 5
213 2 2 3 4 4 4.0 3 2 2
214 3 3 2 5 5 4.0 4 3 4
215 3 3 2 1 4 4.0 2 2 4
216 2 4 2 4 4 4.0 2 4 4
217 5 3 2 2 4 4.0 5 2 1
218 3 2 1 5 5 5.0 1 3 5
219 4 3 4 5 5 5.0 4 4 2
220 4 4 4 4 4 4.0 2 2 2
221 3 2 2 5 5 5.0 4 2 4
222 3 2 2 4 4 4.0 4 2 3
223 3 2 2 4 4 2.0 3 3 3
224 3 2 3 4 5 5.0 2 1 1
225 4 2 2 5 4 3.0 3 3 3
226 2 2 3 5 4 5.0 5 4 3
227 2 2 3 5 5 5.0 4 5 5
228 2 2 2 5 5 2.0 4 3 4
229 4 4 2 4 4 4.0 2 2 2
230 2 2 2 4 4 4.0 2 2 2
231 2 2 2 4 4 4.0 2 4 2
232 4 4 2 4 4 4.0 4 4 4
233 5 4 2 4 5 5.0 5 4 5
234 2 5 2 5 5 5.0 5 3 1
235 2 3 2 5 4 5.0 3 3 1
236 2 4 2 4 5 5.0 4 3 4
237 2 4 2 1 4 1.0 2 3 2
238 3 3 2 4 4 4.0 3 3 3
239 2 1 1 4 4 4.0 4 2 3
240 3 4 5 5 3 4.0 4 4 4
241 2 2 2 4 4 4.0 4 2 2
242 4 3 3 4 4 4.0 3 3 4
243 2 2 2 4 4 4.0 2 2 4
244 4 4 2 5 5 5.0 3 5 3
245 3 3 5 5 5 5.0 3 5 2
246 2 4 2 4 4 2.0 2 2 2
247 1 1 1 5 5 5.0 4 4 4
248 2 2 2 5 5 4.0 4 4 4
249 3 3 3 5 5 5.0 4 5 3
250 4 3 3 3 5 5.0 5 5 3
251 4 3 3 5 5 5.0 3 5 3
252 3 3 2 5 5 3.0 3 4 2
253 2 2 2 5 2 5.0 5 5 5
254 2 5 5 5 5 5.0 5 5 5
255 2 2 2 5 5 5.0 5 5 5
256 5 5 5 5 5 5.0 5 5 5
257 2 2 2 5 5 5.0 5 5 5
258 5 5 5 5 5 5.0 5 5 5
259 2 2 2 5 5 5.0 5 5 5
260 2 5 5 5 5 5.0 5 5 5
261 2 2 2 5 5 5.0 5 5 5
262 2 2 5 5 5 5.0 5 5 5
263 3 1 1 4 5 5.0 2 5 1
264 1 2 2 5 5 4.0 4 3 4
265 5 4 3 3 4 5.0 5 5 3
266 2 2 2 4 3 4.0 2 3 3
267 3 2 1 5 5 4.0 3 2 4
268 3 1 2 5 5 3.0 3 1 3
269 1 1 1 5 5 5.0 3 5 5
270 1 1 1 5 5 5.0 4 1 3
271 5 4 4 4 4 4.0 2 2 4
272 3 2 1 5 5 5.0 5 5 3
273 2 1 1 4 4 4.0 4 4 4
274 1 1 2 5 4 4.0 3 4 2
275 3 4 2 5 5 5.0 4 2 2
276 4 1 1 5 5 5.0 5 5 5
277 2 2 1 5 5 5.0 3 3 4
278 3 2 2 4 4 4.0 4 4 4
279 2 4 2 5 5 4.0 4 2 4
280 5 4 2 5 5 5.0 3 3 4
281 1 1 1 4 4 3.0 3 3 4
282 4 4 2 4 4 4.0 5 4 3
283 2 2 4 5 5 3.0 5 1 3
284 4 4 5 5 5 5.0 4 4 4
285 2 4 2 4 4 4.0 4 4 4
286 4 4 2 4 3 4.0 3 4 4
287 3 4 2 4 4 4.0 5 2 2
288 2 2 3 4 4 4.0 4 4 4
289 2 2 2 4 4 4.0 2 4 4
290 2 2 2 4 4 4.0 4 4 4
291 2 2 2 4 4 4.0 4 4 4
292 4 3 4 4 1 3.0 3 4 3
293 5 4 2 5 4 4.0 5 5 4
294 2 5 2 5 5 5.0 4 4 5
295 2 4 2 4 4 4.0 2 2 4
296 5 4 2 5 5 5.0 5 5 4
297 2 2 2 3 3 4.0 5 5 5
298 2 3 4 3 4 5.0 4 4 5
299 4 3 3 3 4 4.0 4 4 2
300 4 3 3 4 4 4.0 2 2 4
301 3 4 3 5 3 5.0 4 4 5
302 2 2 4 5 4 4.0 5 4 3
303 5 2 4 4 4 5.0 5 5 4
304 2 2 4 5 5 2.0 4 3 4
305 2 2 4 5 5 4.0 5 4 4
306 4 2 3 4 4 4.0 4 2 2
307 3 2 2 4 4 4.0 5 2 4
308 4 3 4 4 4 4.0 4 3 3
309 4 3 4 5 4 4.0 4 4 3
310 2 2 2 4 4 4.0 4 2 2
311 4 4 4 4 4 5.0 5 4 4
312 4 4 4 5 5 5.0 5 5 5
313 3 2 2 4 4 4.0 4 3 2
314 5 2 2 5 5 5.0 5 4 3
315 5 2 2 4 4 5.0 5 5 2
316 4 4 3 4 4 4.0 4 4 4
317 5 4 4 5 5 5.0 3 2 3
318 1 1 1 5 5 5.0 5 5 5
319 1 1 1 5 5 5.0 4 1 1
320 1 1 1 4 4 4.0 3 4 4
321 2 2 2 5 5 5.0 4 4 3
322 3 2 1 3 3 3.0 4 3 4
323 3 2 1 3 3 3.0 4 3 4
324 2 2 2 4 4 4.0 4 4 4
325 2 1 2 5 5 4.0 4 4 3
326 4 4 2 4 4 2.0 2 2 2
327 3 3 3 3 4 4.0 4 3 3
328 1 1 1 4 4 4.0 4 4 2
329 5 3 3 5 5 2.0 5 5 1
330 5 4 3 5 2 2.0 3 1 1
331 5 3 3 3 3 2.0 4 2 2
332 4 4 3 5 5 5.0 2 2 5
333 3 3 3 5 5 3.0 1 1 3
334 5 2 2 2 2 4.0 2 1 1
335 5 2 2 5 5 5.0 3 5 5
336 4 5 2 5 1 2.0 5 4 1
337 4 3 2 5 5 4.0 4 5 5
338 4 2 4 3 4 4.0 4 5 5
339 3 4 4 3 3 4.0 1 1 3
340 4 2 4 5 4 4.0 4 5 5
341 4 1 1 5 5 5.0 3 2 4
342 5 2 3 5 2 3.0 5 5 5
343 3 2 1 5 5 3.0 5 3 3
344 4 2 2 5 4 5.0 5 3 3
345 3 3 2 5 5 5.0 3 3 2
346 5 3 3 3 2 4.0 4 4 2
347 5 2 2 5 4 5.0 5 4 4
348 4 2 2 4 4 4.0 4 2 4
349 5 4 3 5 5 5.0 5 2 4
350 4 4 3 5 5 5.0 3 3 4
351 4 4 2 5 5 5.0 4 4 3
352 4 3 2 5 5 4.0 4 4 4
353 3 2 1 5 5 5.0 4 1 3
354 3 3 3 5 5 5.0 3 2 5
355 4 4 2 5 5 5.0 3 4 4
356 4 3 3 5 5 5.0 4 3 3
357 4 4 3 5 5 5.0 4 3 3
358 4 4 3 4 5 5.0 3 4 4
359 3 4 2 5 5 4.0 3 3 3
360 3 4 3 4 4 4.0 4 4 4
361 2 4 2 4 4 4.0 5 5 5
362 2 2 2 5 4 4.0 4 4 3
363 4 3 2 5 5 5.0 3 3 3
364 4 4 3 5 5 4.0 5 5 3
365 4 3 2 5 5 5.0 5 3 3
366 2 2 2 4 4 4.0 2 2 2
367 4 2 4 4 4 4.0 4 4 4
368 2 2 2 4 4 4.0 4 2 2
369 3 2 3 5 5 3.0 5 3 5
370 3 2 3 5 5 3.0 5 3 5
371 3 2 3 5 5 3.0 5 4 3
372 4 2 3 5 5 3.0 4 5 5
373 3 3 2 5 5 5.0 4 3 5
374 4 3 4 4 3 4.0 2 4 3
375 3 1 2 3 4 5.0 1 2 5
376 4 2 2 4 4 4.0 4 4 4
377 5 4 4 5 4 4.0 5 5 2
378 4 2 2 5 5 5.0 4 4 3
379 2 2 2 5 5 5.0 4 4 3
380 4 2 4 5 4 4.0 4 4 4
381 4 2 5 5 4 4.0 4 4 4
382 4 2 4 4 4 5.0 4 2 4
383 5 3 2 5 5 5.0 2 5 5
384 4 2 2 4 4 4.0 2 2 4
385 4 3 1 5 4 4.0 3 3 1
386 4 3 1 4 4 5.0 2 2 3
387 2 2 2 5 5 5.0 3 1 1
388 5 3 2 5 5 5.0 4 2 4
389 4 3 1 5 5 5.0 5 3 4
390 4 4 2 5 5 5.0 4 4 4
391 4 3 1 5 5 5.0 4 3 3
392 5 4 2 5 5 5.0 5 4 4
393 5 5 3 5 5 5.0 5 4 4
394 5 4 2 5 5 5.0 4 3 3
395 1 1 1 3 4 4.0 3 4 3
396 3 4 3 4 4 5.0 3 3 2
397 4 3 2 4 4 4.0 4 3 4
398 2 2 3 5 4 4.0 3 3 3
399 3 1 2 4 4 3.0 4 4 3
400 2 2 2 5 5 5.0 3 1 1
401 4 4 2 5 5 4.0 5 5 1
402 3 4 1 4 5 3.0 1 4 4
403 4 4 2 5 5 4.0 3 4 3
404 5 3 5 5 4 3.0 3 4 2
405 4 4 1 4 5 4.0 5 1 3
406 1 1 1 3 3 3.0 4 1 3
407 4 3 1 4 4 5.0 2 1 1
408 3 3 2 5 4 3.0 1 1 3
409 3 3 2 5 4 3.0 5 5 3
410 4 1 1 4 3 4.0 4 1 3
411 1 1 1 3 3 4.0 4 4 4
412 3 1 1 3 4 3.0 3 3 3
413 3 4 1 4 4 5.0 3 4 4
414 4 3 2 5 5 5.0 4 4 3
415 4 3 5 5 3 5.0 2 1 1
416 3 3 3 4 4 4.0 3 4 2
417 1 1 1 3 5 4.0 1 3 1
418 5 4 2 2 3 5.0 5 2 5
419 3 3 3 4 4 4.0 4 4 2
420 2 4 2 4 4 4.0 4 4 4
421 1 3 3 5 5 5.0 5 5 2
422 5 2 4 3 3 3.0 4 4 4
423 4 4 3 5 5 5.0 4 4 4
424 1 3 1 4 3 5.0 3 5 4
425 2 2 1 5 5 4.0 4 3 3
426 3 2 1 3 3 4.0 3 3 3
427 1 5 1 5 5 5.0 5 2 4
428 4 4 3 5 4 5.0 4 4 4
429 4 3 2 5 5 4.0 5 4 2
430 4 2 2 5 4 5.0 5 5 4
431 3 2 2 5 4 5.0 4 5 2
432 3 4 3 5 5 5.0 5 3 2
433 5 4 2 5 4 5.0 5 5 5
434 4 2 2 5 5 4.0 4 4 3
435 2 2 3 5 5 5.0 3 3 3
436 4 2 4 5 4 5.0 3 3 3
437 4 2 4 5 4 5.0 5 5 5
438 4 2 3 5 4 5.0 5 5 4
439 1 1 4 5 4 5.0 5 4 4
440 1 1 1 5 5 5.0 5 5 5
441 4 3 4 4 5 3.0 3 2 3
442 5 3 3 3 5 4.0 5 2 2
443 5 3 3 5 5 5.0 3 2 2
444 5 3 3 3 5 5.0 3 2 2
445 3 3 2 4 4 3.0 2 1 2
446 3 2 2 4 5 3.0 3 2 2
447 4 4 2 4 5 5.0 4 4 3
448 4 4 3 5 4 5.0 3 5 3
449 5 5 5 5 5 5.0 5 5 5
450 4 4 4 4 4 4.0 4 4 4
451 4 4 5 4 4 4.0 4 4 3
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453 2 2 2 4 4 4.0 3 3 3
454 2 2 1 4 4 4.0 4 3 3
455 3 1 1 4 4 5.0 5 2 1
456 1 1 1 5 5 5.0 4 2 2
457 2 2 2 4 5 4.0 4 4 4
458 1 2 2 4 5 4.0 4 3 2
459 2 2 2 4 4 4.0 3 2 3
460 2 2 2 5 5 4.0 4 2 2
461 4 3 2 4 4 4.0 4 3 4
462 1 1 1 5 5 5.0 3 2 1
463 2 2 3 4 4 4.0 4 3 3
464 4 4 2 4 5 4.0 4 5 5
465 1 1 1 5 5 5.0 1 1 1
466 3 3 5 5 5 5.0 4 5 4
467 3 3 2 5 5 5.0 3 3 3
468 2 2 2 4 5 5.0 5 4 3
469 4 4 3 3 3 3.0 3 3 3
470 2 2 2 4 5 5.0 5 4 3
471 4 2 3 4 4 3.0 5 3 4
472 4 3 3 3 4 5.0 3 4 4
473 2 2 4 4 2 2.0 4 4 2
474 1 1 2 5 5 5.0 5 5 3
475 4 3 2 5 5 4.0 5 5 4
476 3 2 2 4 5 4.0 5 3 2
477 2 2 2 5 4 4.0 4 2 2
478 5 2 2 5 5 5.0 2 2 2
479 1 2 1 4 5 4.0 2 1 1
480 3 3 3 5 5 4.0 3 3 3
481 5 4 4 5 4 5.0 4 4 3
482 1 1 3 4 4 4.0 5 4 4
483 3 3 3 4 5 3.0 3 3 3
484 2 1 1 4 4 5.0 4 3 2
485 4 4 4 5 5 5.0 3 5 4
486 4 2 2 5 5 4.0 3 4 2
487 3 3 3 5 5 5.0 3 4 3
488 5 5 4 3 5 5.0 5 4 3
489 3 3 4 5 5 5.0 4 3 3
490 4 2 3 5 4 4.0 3 4 4
491 2 2 2 5 5 4.0 4 2 2
492 4 4 3 4 4 4.0 3 3 3
493 5 4 5 5 5 5.0 4 5 4
494 4 4 2 5 5 5.0 2 2 2
495 3 2 3 4 4 5.0 5 3 1
496 5 4 4 4 3 4.0 3 3 4
497 3 3 3 4 3 3.0 3 3 3
498 4 3 3 4 5 3.0 4 3 3
499 3 4 2 5 5 4.0 5 1 3
500 5 5 3 5 5 5.0 5 5 3
501 1 4 1 4 4 4.0 4 1 1
502 4 3 3 5 5 5.0 5 2 1
503 3 5 3 4 5 1.0 5 4 2
504 5 5 3 5 5 5.0 5 4 3
505 4 5 3 1 2 5.0 3 4 2
506 5 4 3 4 3 3.0 2 1 2
507 5 5 3 5 5 5.0 5 4 3
508 3 3 4 5 5 5.0 3 4 3
509 2 2 2 4 4 4.0 4 4 2
510 1 1 1 5 5 5.0 1 1 1
511 4 2 2 4 4 5.0 5 2 3
512 3 2 2 5 5 5.0 4 4 3
513 3 2 2 4 5 4.0 4 5 3
514 4 1 1 4 5 5.0 2 4 1
515 2 2 1 3 4 4.0 2 1 1
516 2 2 2 5 5 5.0 3 3 3
517 3 3 3 5 5 5.0 5 5 5
518 3 3 3 5 5 5.0 5 5 5
519 4 3 3 5 5 5.0 5 5 5
520 4 3 3 5 5 5.0 5 5 5
521 3 3 3 5 5 5.0 5 5 5
522 3 3 3 5 5 5.0 5 5 5
523 3 3 2 5 5 5.0 5 5 5
524 2 2 1 5 5 5.0 5 5 5
525 3 3 3 5 5 5.0 5 5 5
526 3 3 3 5 5 5.0 5 5 5
In [128]:
data_Usage_Factor_Final['Call Friends and Family'] = data_Usage_Factor_Final['Call Friends and Family'].astype(int)
data_Usage_Factor_Final
Out[128]:
Feature All my friends use Old fashioned Prestige Secure Communication Call Friends and Family Mostly use SMS Music or Videos Games
0 3 2 2 4 4 4 2 3 2
1 5 1 3 5 5 5 3 2 3
2 4 2 2 5 5 5 4 4 3
3 4 4 3 4 4 4 4 5 4
4 4 4 4 4 5 5 4 5 5
5 5 4 3 4 4 4 5 5 3
6 1 4 4 5 5 1 1 1 1
7 2 2 2 5 5 5 3 4 4
8 4 4 4 4 4 4 3 4 4
9 2 1 1 5 5 5 3 4 2
10 3 3 2 4 5 3 2 3 2
11 3 2 1 5 5 5 3 5 3
12 4 2 2 5 5 5 2 4 4
13 4 3 2 5 5 3 3 2 1
14 3 3 3 4 5 5 4 5 3
15 4 4 4 3 4 5 5 2 3
16 1 1 1 1 5 1 1 1 1
17 3 3 2 5 5 3 3 3 2
18 2 3 2 5 5 3 3 2 2
19 2 3 2 5 5 3 3 2 3
20 1 1 1 3 5 3 1 1 1
21 5 5 5 2 2 2 5 3 2
22 4 3 2 5 4 3 2 2 2
23 3 4 3 4 5 4 5 4 4
24 2 2 2 5 5 3 5 3 2
25 2 2 2 5 5 5 3 3 3
26 4 4 2 5 5 5 4 4 4
27 4 1 1 5 5 4 4 3 3
28 3 2 3 5 5 5 4 4 3
29 3 3 4 5 5 5 2 1 1
30 4 3 3 4 5 5 3 5 4
31 1 1 1 3 3 4 3 3 2
32 4 3 3 5 5 5 3 4 1
33 5 3 4 3 5 5 5 5 5
34 3 3 3 4 3 5 4 3 3
35 5 4 3 4 4 3 5 4 3
36 4 3 4 2 3 5 2 4 2
37 4 4 5 5 5 4 5 4 5
38 4 4 4 3 1 2 3 4 5
39 3 3 3 3 3 3 3 3 3
40 3 3 3 1 1 4 2 4 2
41 4 3 3 3 3 3 3 3 3
42 2 2 1 1 4 2 3 2 1
43 3 3 3 3 3 3 3 3 3
44 2 1 2 5 5 5 2 3 3
45 2 2 2 5 5 5 5 4 4
46 4 3 5 4 5 3 5 2 4
47 5 4 5 4 5 4 5 4 5
48 4 3 3 4 5 3 2 3 2
49 5 4 4 4 5 5 4 4 4
50 3 3 3 5 4 3 3 4 4
51 4 2 3 4 4 4 4 4 4
52 5 4 3 3 5 3 5 5 4
53 5 4 5 5 3 5 5 5 3
54 5 4 5 4 3 5 4 5 5
55 5 5 5 4 5 4 5 4 5
56 5 5 5 4 5 5 5 4 5
57 5 4 5 3 5 4 5 3 5
58 3 4 3 5 5 4 3 4 4
59 5 2 5 5 4 5 4 5 5
60 5 2 2 4 4 4 2 2 2
61 4 5 3 5 5 4 3 3 3
62 5 4 5 3 5 1 5 5 3
63 5 4 5 3 5 2 5 1 3
64 5 4 5 4 3 5 3 5 2
65 5 4 4 3 5 4 2 2 4
66 3 4 3 5 5 4 5 5 3
67 5 5 5 4 5 5 5 3 3
68 3 4 3 5 5 4 5 4 3
69 4 2 1 4 4 4 4 4 2
70 4 2 3 4 5 4 2 1 2
71 4 2 2 4 4 5 4 3 2
72 2 3 2 4 5 5 4 4 5
73 5 3 3 5 5 5 5 3 5
74 3 2 2 3 4 4 2 2 2
75 2 2 2 5 5 5 4 4 3
76 4 2 2 5 4 5 5 2 2
77 4 4 1 5 5 4 4 3 3
78 4 2 2 4 4 4 4 3 3
79 4 3 4 3 4 5 4 4 3
80 5 3 4 5 3 4 5 3 5
81 2 2 2 4 4 4 3 3 2
82 5 4 3 5 1 1 1 3 5
83 3 3 1 5 5 5 4 1 2
84 5 4 3 5 3 2 5 2 1
85 4 4 4 4 4 4 4 4 4
86 3 2 2 5 5 4 3 4 4
87 3 2 2 4 4 4 4 2 1
88 3 2 2 5 5 4 2 2 2
89 5 4 4 5 5 5 4 4 3
90 5 5 4 5 3 2 4 3 4
91 4 4 4 5 4 4 5 4 4
92 4 3 3 2 5 3 4 4 4
93 3 1 1 5 5 5 1 2 1
94 4 4 3 3 5 3 1 5 3
95 2 2 2 5 4 4 2 2 2
96 4 2 4 4 4 5 5 2 1
97 3 5 5 5 5 5 5 5 5
98 5 3 4 5 3 5 2 3 5
99 2 3 3 4 4 4 2 3 2
100 1 4 1 5 5 4 4 3 2
101 5 3 2 5 5 5 5 5 4
102 4 3 3 4 5 5 3 4 3
103 2 2 2 5 5 5 2 2 2
104 2 2 2 5 5 5 2 2 2
105 3 4 4 5 5 5 1 1 1
106 3 4 3 4 4 3 4 4 4
107 4 3 5 4 3 4 2 3 2
108 4 2 2 3 5 4 3 3 2
109 2 2 2 5 5 5 5 5 5
110 2 2 2 5 4 4 4 4 1
111 3 3 2 4 5 5 1 1 2
112 4 3 4 4 5 4 4 2 2
113 3 3 3 5 5 4 4 5 4
114 2 2 4 5 5 5 4 4 3
115 3 3 2 3 3 5 3 2 2
116 5 4 4 4 5 5 3 4 3
117 3 3 4 5 5 5 5 5 3
118 3 3 3 4 4 5 4 4 2
119 2 2 2 5 5 5 4 4 3
120 2 3 2 5 5 5 1 2 2
121 4 3 4 4 4 4 4 4 4
122 5 5 4 4 5 5 3 5 3
123 5 3 5 5 4 5 5 5 5
124 2 4 2 5 5 4 3 4 2
125 5 5 4 5 5 5 3 3 3
126 5 3 2 5 5 5 2 2 2
127 2 4 1 5 5 5 4 1 2
128 1 1 1 1 1 1 1 1 1
129 5 5 5 5 5 5 5 5 5
130 5 5 5 5 5 5 5 5 5
131 2 1 1 3 5 4 1 1 1
132 4 2 1 5 5 5 1 2 2
133 1 1 1 4 5 3 2 1 1
134 3 4 2 3 4 4 4 3 1
135 3 1 1 5 5 5 3 2 2
136 5 3 2 5 5 5 3 2 1
137 5 3 2 4 4 5 4 3 2
138 3 5 4 3 5 3 5 4 3
139 3 3 2 4 5 5 5 3 2
140 2 2 2 4 5 4 3 4 3
141 2 3 2 4 4 4 3 2 3
142 4 3 4 5 5 5 5 2 2
143 2 2 2 4 4 4 4 2 2
144 5 3 4 5 5 5 2 5 5
145 5 5 4 5 5 5 5 4 3
146 2 2 1 5 5 5 5 2 2
147 5 4 4 5 5 5 4 4 4
148 4 4 3 5 5 5 3 5 5
149 1 1 1 5 5 5 1 1 1
150 2 3 2 4 4 4 3 2 4
151 5 5 3 4 5 5 5 3 3
152 2 2 2 5 5 5 5 4 3
153 3 3 3 4 4 4 3 3 2
154 4 5 1 4 5 5 4 1 1
155 3 4 4 5 4 5 4 3 3
156 4 4 2 4 5 3 2 2 2
157 3 2 3 4 4 4 3 4 2
158 2 2 2 4 5 4 2 2 1
159 2 2 1 4 4 4 3 2 2
160 3 4 3 4 4 4 4 4 3
161 3 3 2 5 5 5 5 4 4
162 2 5 2 5 5 5 2 5 4
163 4 3 2 5 5 5 5 5 4
164 2 2 4 5 5 5 2 5 5
165 5 5 5 4 5 5 5 4 2
166 5 1 2 5 5 5 5 3 3
167 2 2 2 5 5 5 5 2 5
168 4 2 2 5 5 5 5 3 3
169 2 2 2 5 5 5 5 5 5
170 4 2 2 5 5 4 2 3 3
171 4 3 3 4 4 5 5 5 2
172 3 2 2 5 5 5 4 2 2
173 2 2 1 5 5 4 5 3 3
174 2 3 2 4 5 5 5 4 3
175 2 4 2 5 5 5 5 5 5
176 2 1 1 5 5 5 1 5 1
177 2 2 3 4 5 5 5 2 5
178 2 2 2 4 4 4 3 4 4
179 4 3 5 5 5 4 5 4 3
180 5 5 5 5 3 5 5 2 2
181 3 2 2 4 4 4 3 2 2
182 4 1 1 5 5 5 4 5 4
183 4 2 2 4 4 4 4 5 4
184 2 3 2 4 5 4 4 4 5
185 1 1 1 4 3 4 3 2 1
186 2 4 2 4 5 5 2 1 2
187 2 2 2 5 5 5 5 5 5
188 5 4 1 5 1 3 3 5 4
189 1 1 1 5 5 5 3 5 3
190 3 3 2 5 5 5 3 5 3
191 2 2 2 5 5 5 3 5 3
192 5 5 5 5 5 5 5 5 4
193 4 4 4 5 5 5 5 5 5
194 4 3 3 5 5 5 5 5 5
195 2 2 2 5 5 5 5 5 5
196 2 2 2 4 5 5 5 5 5
197 2 2 2 2 5 5 5 5 5
198 2 2 2 5 5 5 5 5 3
199 3 3 3 5 5 5 5 5 3
200 3 4 2 4 4 4 4 4 3
201 3 3 2 4 4 4 4 4 4
202 2 2 2 4 4 4 4 2 2
203 5 2 2 4 5 5 5 2 2
204 5 2 3 5 5 5 5 1 1
206 3 2 2 5 5 5 3 2 2
207 5 2 2 5 5 4 4 4 5
208 4 3 4 4 4 3 3 3 3
209 3 3 5 5 5 5 3 3 3
210 4 2 2 4 4 4 4 2 4
211 1 1 1 5 5 4 3 1 1
212 4 4 3 3 5 5 5 5 5
213 2 2 3 4 4 4 3 2 2
214 3 3 2 5 5 4 4 3 4
215 3 3 2 1 4 4 2 2 4
216 2 4 2 4 4 4 2 4 4
217 5 3 2 2 4 4 5 2 1
218 3 2 1 5 5 5 1 3 5
219 4 3 4 5 5 5 4 4 2
220 4 4 4 4 4 4 2 2 2
221 3 2 2 5 5 5 4 2 4
222 3 2 2 4 4 4 4 2 3
223 3 2 2 4 4 2 3 3 3
224 3 2 3 4 5 5 2 1 1
225 4 2 2 5 4 3 3 3 3
226 2 2 3 5 4 5 5 4 3
227 2 2 3 5 5 5 4 5 5
228 2 2 2 5 5 2 4 3 4
229 4 4 2 4 4 4 2 2 2
230 2 2 2 4 4 4 2 2 2
231 2 2 2 4 4 4 2 4 2
232 4 4 2 4 4 4 4 4 4
233 5 4 2 4 5 5 5 4 5
234 2 5 2 5 5 5 5 3 1
235 2 3 2 5 4 5 3 3 1
236 2 4 2 4 5 5 4 3 4
237 2 4 2 1 4 1 2 3 2
238 3 3 2 4 4 4 3 3 3
239 2 1 1 4 4 4 4 2 3
240 3 4 5 5 3 4 4 4 4
241 2 2 2 4 4 4 4 2 2
242 4 3 3 4 4 4 3 3 4
243 2 2 2 4 4 4 2 2 4
244 4 4 2 5 5 5 3 5 3
245 3 3 5 5 5 5 3 5 2
246 2 4 2 4 4 2 2 2 2
247 1 1 1 5 5 5 4 4 4
248 2 2 2 5 5 4 4 4 4
249 3 3 3 5 5 5 4 5 3
250 4 3 3 3 5 5 5 5 3
251 4 3 3 5 5 5 3 5 3
252 3 3 2 5 5 3 3 4 2
253 2 2 2 5 2 5 5 5 5
254 2 5 5 5 5 5 5 5 5
255 2 2 2 5 5 5 5 5 5
256 5 5 5 5 5 5 5 5 5
257 2 2 2 5 5 5 5 5 5
258 5 5 5 5 5 5 5 5 5
259 2 2 2 5 5 5 5 5 5
260 2 5 5 5 5 5 5 5 5
261 2 2 2 5 5 5 5 5 5
262 2 2 5 5 5 5 5 5 5
263 3 1 1 4 5 5 2 5 1
264 1 2 2 5 5 4 4 3 4
265 5 4 3 3 4 5 5 5 3
266 2 2 2 4 3 4 2 3 3
267 3 2 1 5 5 4 3 2 4
268 3 1 2 5 5 3 3 1 3
269 1 1 1 5 5 5 3 5 5
270 1 1 1 5 5 5 4 1 3
271 5 4 4 4 4 4 2 2 4
272 3 2 1 5 5 5 5 5 3
273 2 1 1 4 4 4 4 4 4
274 1 1 2 5 4 4 3 4 2
275 3 4 2 5 5 5 4 2 2
276 4 1 1 5 5 5 5 5 5
277 2 2 1 5 5 5 3 3 4
278 3 2 2 4 4 4 4 4 4
279 2 4 2 5 5 4 4 2 4
280 5 4 2 5 5 5 3 3 4
281 1 1 1 4 4 3 3 3 4
282 4 4 2 4 4 4 5 4 3
283 2 2 4 5 5 3 5 1 3
284 4 4 5 5 5 5 4 4 4
285 2 4 2 4 4 4 4 4 4
286 4 4 2 4 3 4 3 4 4
287 3 4 2 4 4 4 5 2 2
288 2 2 3 4 4 4 4 4 4
289 2 2 2 4 4 4 2 4 4
290 2 2 2 4 4 4 4 4 4
291 2 2 2 4 4 4 4 4 4
292 4 3 4 4 1 3 3 4 3
293 5 4 2 5 4 4 5 5 4
294 2 5 2 5 5 5 4 4 5
295 2 4 2 4 4 4 2 2 4
296 5 4 2 5 5 5 5 5 4
297 2 2 2 3 3 4 5 5 5
298 2 3 4 3 4 5 4 4 5
299 4 3 3 3 4 4 4 4 2
300 4 3 3 4 4 4 2 2 4
301 3 4 3 5 3 5 4 4 5
302 2 2 4 5 4 4 5 4 3
303 5 2 4 4 4 5 5 5 4
304 2 2 4 5 5 2 4 3 4
305 2 2 4 5 5 4 5 4 4
306 4 2 3 4 4 4 4 2 2
307 3 2 2 4 4 4 5 2 4
308 4 3 4 4 4 4 4 3 3
309 4 3 4 5 4 4 4 4 3
310 2 2 2 4 4 4 4 2 2
311 4 4 4 4 4 5 5 4 4
312 4 4 4 5 5 5 5 5 5
313 3 2 2 4 4 4 4 3 2
314 5 2 2 5 5 5 5 4 3
315 5 2 2 4 4 5 5 5 2
316 4 4 3 4 4 4 4 4 4
317 5 4 4 5 5 5 3 2 3
318 1 1 1 5 5 5 5 5 5
319 1 1 1 5 5 5 4 1 1
320 1 1 1 4 4 4 3 4 4
321 2 2 2 5 5 5 4 4 3
322 3 2 1 3 3 3 4 3 4
323 3 2 1 3 3 3 4 3 4
324 2 2 2 4 4 4 4 4 4
325 2 1 2 5 5 4 4 4 3
326 4 4 2 4 4 2 2 2 2
327 3 3 3 3 4 4 4 3 3
328 1 1 1 4 4 4 4 4 2
329 5 3 3 5 5 2 5 5 1
330 5 4 3 5 2 2 3 1 1
331 5 3 3 3 3 2 4 2 2
332 4 4 3 5 5 5 2 2 5
333 3 3 3 5 5 3 1 1 3
334 5 2 2 2 2 4 2 1 1
335 5 2 2 5 5 5 3 5 5
336 4 5 2 5 1 2 5 4 1
337 4 3 2 5 5 4 4 5 5
338 4 2 4 3 4 4 4 5 5
339 3 4 4 3 3 4 1 1 3
340 4 2 4 5 4 4 4 5 5
341 4 1 1 5 5 5 3 2 4
342 5 2 3 5 2 3 5 5 5
343 3 2 1 5 5 3 5 3 3
344 4 2 2 5 4 5 5 3 3
345 3 3 2 5 5 5 3 3 2
346 5 3 3 3 2 4 4 4 2
347 5 2 2 5 4 5 5 4 4
348 4 2 2 4 4 4 4 2 4
349 5 4 3 5 5 5 5 2 4
350 4 4 3 5 5 5 3 3 4
351 4 4 2 5 5 5 4 4 3
352 4 3 2 5 5 4 4 4 4
353 3 2 1 5 5 5 4 1 3
354 3 3 3 5 5 5 3 2 5
355 4 4 2 5 5 5 3 4 4
356 4 3 3 5 5 5 4 3 3
357 4 4 3 5 5 5 4 3 3
358 4 4 3 4 5 5 3 4 4
359 3 4 2 5 5 4 3 3 3
360 3 4 3 4 4 4 4 4 4
361 2 4 2 4 4 4 5 5 5
362 2 2 2 5 4 4 4 4 3
363 4 3 2 5 5 5 3 3 3
364 4 4 3 5 5 4 5 5 3
365 4 3 2 5 5 5 5 3 3
366 2 2 2 4 4 4 2 2 2
367 4 2 4 4 4 4 4 4 4
368 2 2 2 4 4 4 4 2 2
369 3 2 3 5 5 3 5 3 5
370 3 2 3 5 5 3 5 3 5
371 3 2 3 5 5 3 5 4 3
372 4 2 3 5 5 3 4 5 5
373 3 3 2 5 5 5 4 3 5
374 4 3 4 4 3 4 2 4 3
375 3 1 2 3 4 5 1 2 5
376 4 2 2 4 4 4 4 4 4
377 5 4 4 5 4 4 5 5 2
378 4 2 2 5 5 5 4 4 3
379 2 2 2 5 5 5 4 4 3
380 4 2 4 5 4 4 4 4 4
381 4 2 5 5 4 4 4 4 4
382 4 2 4 4 4 5 4 2 4
383 5 3 2 5 5 5 2 5 5
384 4 2 2 4 4 4 2 2 4
385 4 3 1 5 4 4 3 3 1
386 4 3 1 4 4 5 2 2 3
387 2 2 2 5 5 5 3 1 1
388 5 3 2 5 5 5 4 2 4
389 4 3 1 5 5 5 5 3 4
390 4 4 2 5 5 5 4 4 4
391 4 3 1 5 5 5 4 3 3
392 5 4 2 5 5 5 5 4 4
393 5 5 3 5 5 5 5 4 4
394 5 4 2 5 5 5 4 3 3
395 1 1 1 3 4 4 3 4 3
396 3 4 3 4 4 5 3 3 2
397 4 3 2 4 4 4 4 3 4
398 2 2 3 5 4 4 3 3 3
399 3 1 2 4 4 3 4 4 3
400 2 2 2 5 5 5 3 1 1
401 4 4 2 5 5 4 5 5 1
402 3 4 1 4 5 3 1 4 4
403 4 4 2 5 5 4 3 4 3
404 5 3 5 5 4 3 3 4 2
405 4 4 1 4 5 4 5 1 3
406 1 1 1 3 3 3 4 1 3
407 4 3 1 4 4 5 2 1 1
408 3 3 2 5 4 3 1 1 3
409 3 3 2 5 4 3 5 5 3
410 4 1 1 4 3 4 4 1 3
411 1 1 1 3 3 4 4 4 4
412 3 1 1 3 4 3 3 3 3
413 3 4 1 4 4 5 3 4 4
414 4 3 2 5 5 5 4 4 3
415 4 3 5 5 3 5 2 1 1
416 3 3 3 4 4 4 3 4 2
417 1 1 1 3 5 4 1 3 1
418 5 4 2 2 3 5 5 2 5
419 3 3 3 4 4 4 4 4 2
420 2 4 2 4 4 4 4 4 4
421 1 3 3 5 5 5 5 5 2
422 5 2 4 3 3 3 4 4 4
423 4 4 3 5 5 5 4 4 4
424 1 3 1 4 3 5 3 5 4
425 2 2 1 5 5 4 4 3 3
426 3 2 1 3 3 4 3 3 3
427 1 5 1 5 5 5 5 2 4
428 4 4 3 5 4 5 4 4 4
429 4 3 2 5 5 4 5 4 2
430 4 2 2 5 4 5 5 5 4
431 3 2 2 5 4 5 4 5 2
432 3 4 3 5 5 5 5 3 2
433 5 4 2 5 4 5 5 5 5
434 4 2 2 5 5 4 4 4 3
435 2 2 3 5 5 5 3 3 3
436 4 2 4 5 4 5 3 3 3
437 4 2 4 5 4 5 5 5 5
438 4 2 3 5 4 5 5 5 4
439 1 1 4 5 4 5 5 4 4
440 1 1 1 5 5 5 5 5 5
441 4 3 4 4 5 3 3 2 3
442 5 3 3 3 5 4 5 2 2
443 5 3 3 5 5 5 3 2 2
444 5 3 3 3 5 5 3 2 2
445 3 3 2 4 4 3 2 1 2
446 3 2 2 4 5 3 3 2 2
447 4 4 2 4 5 5 4 4 3
448 4 4 3 5 4 5 3 5 3
449 5 5 5 5 5 5 5 5 5
450 4 4 4 4 4 4 4 4 4
451 4 4 5 4 4 4 4 4 3
452 2 2 2 4 5 4 4 3 3
453 2 2 2 4 4 4 3 3 3
454 2 2 1 4 4 4 4 3 3
455 3 1 1 4 4 5 5 2 1
456 1 1 1 5 5 5 4 2 2
457 2 2 2 4 5 4 4 4 4
458 1 2 2 4 5 4 4 3 2
459 2 2 2 4 4 4 3 2 3
460 2 2 2 5 5 4 4 2 2
461 4 3 2 4 4 4 4 3 4
462 1 1 1 5 5 5 3 2 1
463 2 2 3 4 4 4 4 3 3
464 4 4 2 4 5 4 4 5 5
465 1 1 1 5 5 5 1 1 1
466 3 3 5 5 5 5 4 5 4
467 3 3 2 5 5 5 3 3 3
468 2 2 2 4 5 5 5 4 3
469 4 4 3 3 3 3 3 3 3
470 2 2 2 4 5 5 5 4 3
471 4 2 3 4 4 3 5 3 4
472 4 3 3 3 4 5 3 4 4
473 2 2 4 4 2 2 4 4 2
474 1 1 2 5 5 5 5 5 3
475 4 3 2 5 5 4 5 5 4
476 3 2 2 4 5 4 5 3 2
477 2 2 2 5 4 4 4 2 2
478 5 2 2 5 5 5 2 2 2
479 1 2 1 4 5 4 2 1 1
480 3 3 3 5 5 4 3 3 3
481 5 4 4 5 4 5 4 4 3
482 1 1 3 4 4 4 5 4 4
483 3 3 3 4 5 3 3 3 3
484 2 1 1 4 4 5 4 3 2
485 4 4 4 5 5 5 3 5 4
486 4 2 2 5 5 4 3 4 2
487 3 3 3 5 5 5 3 4 3
488 5 5 4 3 5 5 5 4 3
489 3 3 4 5 5 5 4 3 3
490 4 2 3 5 4 4 3 4 4
491 2 2 2 5 5 4 4 2 2
492 4 4 3 4 4 4 3 3 3
493 5 4 5 5 5 5 4 5 4
494 4 4 2 5 5 5 2 2 2
495 3 2 3 4 4 5 5 3 1
496 5 4 4 4 3 4 3 3 4
497 3 3 3 4 3 3 3 3 3
498 4 3 3 4 5 3 4 3 3
499 3 4 2 5 5 4 5 1 3
500 5 5 3 5 5 5 5 5 3
501 1 4 1 4 4 4 4 1 1
502 4 3 3 5 5 5 5 2 1
503 3 5 3 4 5 1 5 4 2
504 5 5 3 5 5 5 5 4 3
505 4 5 3 1 2 5 3 4 2
506 5 4 3 4 3 3 2 1 2
507 5 5 3 5 5 5 5 4 3
508 3 3 4 5 5 5 3 4 3
509 2 2 2 4 4 4 4 4 2
510 1 1 1 5 5 5 1 1 1
511 4 2 2 4 4 5 5 2 3
512 3 2 2 5 5 5 4 4 3
513 3 2 2 4 5 4 4 5 3
514 4 1 1 4 5 5 2 4 1
515 2 2 1 3 4 4 2 1 1
516 2 2 2 5 5 5 3 3 3
517 3 3 3 5 5 5 5 5 5
518 3 3 3 5 5 5 5 5 5
519 4 3 3 5 5 5 5 5 5
520 4 3 3 5 5 5 5 5 5
521 3 3 3 5 5 5 5 5 5
522 3 3 3 5 5 5 5 5 5
523 3 3 2 5 5 5 5 5 5
524 2 2 1 5 5 5 5 5 5
525 3 3 3 5 5 5 5 5 5
526 3 3 3 5 5 5 5 5 5
In [129]:
data_Usage_Factor_Final.shape
Out[129]:
(526, 9)
In [130]:
round(data_Usage_Factor_Final.corr(),3).style.applymap(highlight_cell)
Out[130]:
Feature All my friends use Old fashioned Prestige Secure Communication Call Friends and Family Mostly use SMS Music or Videos Games
Feature                  
All my friends use 1.000000 0.499000 0.473000 -0.001000 -0.090000 0.055000 0.176000 0.153000 0.148000
Old fashioned 0.499000 1.000000 0.498000 -0.002000 -0.013000 -0.001000 0.170000 0.176000 0.165000
Prestige 0.473000 0.498000 1.000000 0.018000 -0.079000 0.008000 0.217000 0.261000 0.247000
Secure -0.001000 -0.002000 0.018000 1.000000 0.436000 0.363000 0.180000 0.172000 0.155000
Communication -0.090000 -0.013000 -0.079000 0.436000 1.000000 0.383000 0.132000 0.074000 0.082000
Call Friends and Family 0.055000 -0.001000 0.008000 0.363000 0.383000 1.000000 0.192000 0.227000 0.179000
Mostly use SMS 0.176000 0.170000 0.217000 0.180000 0.132000 0.192000 1.000000 0.430000 0.359000
Music or Videos 0.153000 0.176000 0.261000 0.172000 0.074000 0.227000 0.430000 1.000000 0.543000
Games 0.148000 0.165000 0.247000 0.155000 0.082000 0.179000 0.359000 0.543000 1.000000
In [131]:
import pandas as pd
import numpy as np
from scipy.stats import pearsonr

# Assuming 'df' is your DataFrame
# Create a correlation matrix
correlation_matrix = data_Usage_Factor_Final.corr()

# Create an empty DataFrame for p-values
p_values_matrix = pd.DataFrame(np.zeros_like(correlation_matrix)
                               , columns=correlation_matrix.columns, index=correlation_matrix.index)

# Calculate p-values for each pair of variables
for i in range(len(correlation_matrix.columns)):
    for j in range(i+1, len(correlation_matrix.columns)):
        corr, p_value = pearsonr(data_Usage_Factor_Final[correlation_matrix.columns[i]]
                                 , data_Usage_Factor_Final[correlation_matrix.columns[j]])
        p_values_matrix.iloc[i, j] = p_value
        p_values_matrix.iloc[j, i] = p_value

# Print the correlation matrix
print("Correlation Matrix:")
display(correlation_matrix.style.applymap(highlight_cell))

# Print the p-values matrix
print("\nP-Values Matrix:")
display(round(p_values_matrix,3))
Correlation Matrix:
Feature All my friends use Old fashioned Prestige Secure Communication Call Friends and Family Mostly use SMS Music or Videos Games
Feature                  
All my friends use 1.000000 0.498746 0.472517 -0.001349 -0.089704 0.055044 0.175527 0.152900 0.147724
Old fashioned 0.498746 1.000000 0.498092 -0.001736 -0.012605 -0.000598 0.170099 0.175803 0.164977
Prestige 0.472517 0.498092 1.000000 0.017706 -0.078796 0.007512 0.217011 0.260811 0.246758
Secure -0.001349 -0.001736 0.017706 1.000000 0.435566 0.362848 0.179875 0.172270 0.154889
Communication -0.089704 -0.012605 -0.078796 0.435566 1.000000 0.382600 0.132334 0.074264 0.081835
Call Friends and Family 0.055044 -0.000598 0.007512 0.362848 0.382600 1.000000 0.192116 0.227030 0.178823
Mostly use SMS 0.175527 0.170099 0.217011 0.179875 0.132334 0.192116 1.000000 0.430105 0.359471
Music or Videos 0.152900 0.175803 0.260811 0.172270 0.074264 0.227030 0.430105 1.000000 0.543107
Games 0.147724 0.164977 0.246758 0.154889 0.081835 0.178823 0.359471 0.543107 1.000000
P-Values Matrix:
Feature All my friends use Old fashioned Prestige Secure Communication Call Friends and Family Mostly use SMS Music or Videos Games
Feature
All my friends use 0.000 0.000 0.000 0.975 0.040 0.208 0.000 0.000 0.001
Old fashioned 0.000 0.000 0.000 0.968 0.773 0.989 0.000 0.000 0.000
Prestige 0.000 0.000 0.000 0.685 0.071 0.864 0.000 0.000 0.000
Secure 0.975 0.968 0.685 0.000 0.000 0.000 0.000 0.000 0.000
Communication 0.040 0.773 0.071 0.000 0.000 0.000 0.002 0.089 0.061
Call Friends and Family 0.208 0.989 0.864 0.000 0.000 0.000 0.000 0.000 0.000
Mostly use SMS 0.000 0.000 0.000 0.000 0.002 0.000 0.000 0.000 0.000
Music or Videos 0.000 0.000 0.000 0.000 0.089 0.000 0.000 0.000 0.000
Games 0.001 0.000 0.000 0.000 0.061 0.000 0.000 0.000 0.000
In [132]:
import pandas as pd
import numpy as np
from factor_analyzer import FactorAnalyzer
from factor_analyzer.factor_analyzer import calculate_kmo
from factor_analyzer.factor_analyzer import calculate_bartlett_sphericity

# Assuming 'data_HS_Factor' is your DataFrame
# Specify the number of factors
n_factors = 9

# Initialize FactorAnalyzer
fa = FactorAnalyzer(n_factors, rotation=None)

# Fit the model
fa.fit(data_Usage_Factor_Final)

# Obtain the factor loadings
factor_loadings = pd.DataFrame(fa.loadings_, index=data_Usage_Factor_Final.columns)

# Calculate Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy

kmo_per_variable, kmo_total = calculate_kmo(data_Usage_Factor_Final)

# Print the KMO values
print("Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy per variable:")
print(kmo_per_variable)
print("\nKaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy (Total):", kmo_total)

chi2_statistic,p_value = calculate_bartlett_sphericity(data_Usage_Factor_Final)

# Print the test statistics
print("\nBartlett's Test of Sphericity:")
print(f"Chi-Square Statistic: {chi2_statistic}")
print(f"P-value: {p_value}")
Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy per variable:
[0.71724114 0.71211378 0.75787734 0.71045542 0.63463618 0.73264628
 0.82660943 0.70922474 0.73750649]

Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy (Total): 0.7266222617596327

Bartlett's Test of Sphericity:
Chi-Square Statistic: 993.8445053382605
P-value: 3.092633300143612e-185

4.1.1 KMO and Bartelett’s Test¶

From the KMO and Bartelett’s Test table we can observe that the KMO value is 0.727. This value falls in the range 0.7-0.8. From the recommendation of Kaiser and Rice we can proceed with the data for factor analysis. From the table we also note that the Bartlett’s test is significant. This indicates that the data under consideration is useful for factor analysis as the variables are not independent.

In [133]:
from scipy.cluster.hierarchy import linkage, dendrogram

# Assuming 'data_HS_Factor' is your data matrix
# Transpose the data matrix to cluster on columns
data_transposed = data_Usage_Factor_Final.T

# Perform hierarchical clustering with single linkage
linkage_matrix = linkage(data_transposed, method='single')

# Create a dendrogram
#dendrogram(linkage_matrix, orientation='top', labels=data_HS_Factor.columns, distance_sort='descending', show_leaf_counts=True)

# Create a dendrogram with transposed orientation
dendrogram(linkage_matrix, orientation='right', labels=data_Usage_Factor_Final.columns
           , distance_sort='descending', show_leaf_counts=True)

# Add labels and title
plt.xlabel('Feature Index')
plt.ylabel('Distance')
plt.title('Dendrogram with Single Linkage on Columns')

# Show the plot
plt.show()

From the Dendrogram, it is clearly evident that there are three groups of variables, signifying different aspects. This suggest that there are correlations between variables as well as the number of factors that can be extracted.

In [134]:
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans

# Assuming 'data_HS_Factor' is your data matrix
# Transpose the data matrix to cluster on columns
data_transposed = data_Usage_Factor_Final.T

# Specify the number of clusters (k)
num_clusters = 3  # You can adjust this based on your requirements

# Perform k-means clustering
kmeans = KMeans(n_clusters=num_clusters, random_state=42)
cluster_labels = kmeans.fit_predict(data_transposed)

# Visualize the results
plt.figure(figsize=(6, 4))
plt.scatter(data_transposed.index, cluster_labels, c=cluster_labels, cmap='viridis', marker='o', s=50)
plt.title('K-Means Clustering on Columns')
plt.xticks(rotation=90)
plt.xlabel('Feature Index')
plt.ylabel('Cluster Label')
plt.show()
C:\Users\vkakarla\AppData\Local\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1446: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.
  warnings.warn(
In [135]:
import matplotlib.pyplot as plt
import numpy as np
from factor_analyzer import FactorAnalyzer

# Assuming 'data_HS_Factor' is your DataFrame
# Specify a range of factor numbers
num_factors_range = range(1, len(data_Usage_Factor_Final.columns) + 1)

# Create an empty list to store eigenvalues
eigenvalues = []

# Iterate over different numbers of factors
for n_factors in num_factors_range:
    fa = FactorAnalyzer(n_factors, method='principal', rotation='varimax')
    fa.fit(data_Usage_Factor)
    eigenvalues.append(fa.get_eigenvalues()[0][n_factors - 1])  # Extract the eigenvalue for the specified number of factors

# Plot the scree plot
plt.plot(num_factors_range, eigenvalues, marker='o')
plt.title('Scree Plot')
plt.xlabel('Number of Factors')
plt.ylabel('Eigenvalues')
plt.show()
C:\Users\vkakarla\AppData\Local\anaconda3\lib\site-packages\factor_analyzer\factor_analyzer.py:663: UserWarning: No rotation will be performed when the number of factors equals 1.
  warnings.warn(

4.1.2 Communalities¶

The Communalities Table shows that all the variables have their communalities above 0.5 and the maximum communality value is 0.720. We also note that most of the values are above 0.6. From this we can say that the extracted factors explains more than 60 % of variation in majority of the variables.

In [136]:
import pandas as pd
from factor_analyzer import FactorAnalyzer

# Assuming 'data_HS_Factor' is your DataFrame
# Specify the number of factors
n_factors = 3

#The rotation must be one of the following: 
#['varimax', 'oblimax', 'quartimax', 'equamax', 'geomin_ort', 'promax', 'oblimin', 'quartimin', 'geomin_obl', None]
# Defaults to ‘promax’.

# Create a FactorAnalyzer object
fa_woer = FactorAnalyzer(len(data_Usage_Factor_Final.columns),method='principal',rotation=None) #without extracation and rotation
fa_wer  = FactorAnalyzer(n_factors,method='principal', rotation='varimax')
 
# Fit the factor model to your data
fa_woer.fit(data_Usage_Factor_Final)
fa_wer.fit(data_Usage_Factor_Final)

# Get the variable names
features = data_Usage_Factor_Final.columns

# Get initial communalities
communalities_without_extraction_rotation = fa_woer.get_communalities()

# Get extracted communalities
communalities_with_extraction_rotation = fa_wer.get_communalities()

# Create a DataFrame with feature, initial communality, and extracted communality
communalities_table = pd.DataFrame({    'Feature': features,    
                                    'Communalities (Initial)': communalities_without_extraction_rotation,
                                    'Communalities (With Extraction and Rotation)': communalities_with_extraction_rotation,
                                   })

# Display the communalities table
# print('\n',features)
# print('\n',communalities_without_rotation)
# print('\n',communalities_with_rotation,'\n')

communalities_table.style.applymap(highlight_cell)
Out[136]:
  Feature Communalities (Initial) Communalities (With Extraction and Rotation)
0 All my friends use 1.000000 0.670556
1 Old fashioned 1.000000 0.686274
2 Prestige 1.000000 0.644226
3 Secure 1.000000 0.604510
4 Communication 1.000000 0.664007
5 Call Friends and Family 1.000000 0.541632
6 Mostly use SMS 1.000000 0.503677
7 Music or Videos 1.000000 0.719980
8 Games 1.000000 0.664838

4.1.3 Total Variance Explained¶

Consider the Total Variance Explained Table. From the table it can be seen that there are three factors extracted and the three factors explains 63.330 % total variation. The first factor explains 29.288 % of variation. The second factor explains 21.132% of variation and the third factor explains 12.909 % of variation.

In [137]:
import pandas as pd
from factor_analyzer import FactorAnalyzer

# Assuming 'data_HS_Factor' is your DataFrame
# Specify the number of factors
n_factors = 3

# Create a FactorAnalyzer object
fa = FactorAnalyzer(n_factors, method='principal', rotation='varimax')

# Fit the factor model to your data
fa.fit(data_Usage_Factor_Final)

# Get the explained variance
explained_variance = fa.get_factor_variance()

# Get additional information
initial_eigenvalues = fa.get_eigenvalues()[0]
percentage_of_variance = initial_eigenvalues / sum(initial_eigenvalues) * 100
cumulative_percentage_of_variance = percentage_of_variance.cumsum()

rotation_sums_squared_loadings = fa.get_factor_variance()[0]
percentage_of_rotation_sums_squared_loadings = fa.get_factor_variance()[1]*100
cumulative_percentage_of_rotation_sums_squared_loadings = fa.get_factor_variance()[2]*100

intial_variance_table = pd.DataFrame({
    'Component': [f'{i + 1}' for i in range(len(data_Usage_Factor_Final.columns))],
    'Initial Eigenvalues': initial_eigenvalues,
    'Initial Eigenvalues (% of Variance)': percentage_of_variance,
    'Initial Eigenvalues (%Cumulative of Variance)': cumulative_percentage_of_variance,

})

factor_variance_table = pd.DataFrame({
    'Component': [f'{i + 1}' for i in range(n_factors)],
    'Rotation SS Loadings': rotation_sums_squared_loadings,
    'Rotation SS Loadings (% of Variance)': percentage_of_rotation_sums_squared_loadings,
    'Rotation SS Loadings (Cumulative of Variance)': cumulative_percentage_of_rotation_sums_squared_loadings
})

# Combine both dataframes
combined_table = pd.merge(intial_variance_table, factor_variance_table, on='Component', how='left')

# Display the combined table
print("Total Variance Explained Table:")
round(combined_table,3)
Total Variance Explained Table:
Out[137]:
Component Initial Eigenvalues Initial Eigenvalues (% of Variance) Initial Eigenvalues (%Cumulative of Variance) Rotation SS Loadings Rotation SS Loadings (% of Variance) Rotation SS Loadings (Cumulative of Variance)
0 1 2.636 29.288 29.288 1.926 21.400 21.400
1 2 1.902 21.132 50.421 1.796 19.957 41.356
2 3 1.162 12.909 63.330 1.978 21.974 63.330
3 4 0.669 7.431 70.761 NaN NaN NaN
4 5 0.655 7.278 78.039 NaN NaN NaN
5 6 0.578 6.424 84.463 NaN NaN NaN
6 7 0.502 5.577 90.040 NaN NaN NaN
7 8 0.465 5.171 95.211 NaN NaN NaN
8 9 0.431 4.789 100.000 NaN NaN NaN

4.1.4 Component Matrix¶

From the Unrotated Factor Solution we can observe that 6 variables are loaded on the first factor and three variables are loaded on the second factor. And the third factor does not have a better loadings to the variables than the other two factors. Then it is difficult to make an interpretation of the factors. To make it easy we rotate the factors using varimax rotation.

In [138]:
import pandas as pd
from factor_analyzer import FactorAnalyzer

# Assuming 'data_HS_Factor' is your DataFrame
# Specify the number of factors
n_factors = 3

# Create a FactorAnalyzer object
fa = FactorAnalyzer(n_factors,method='principal', rotation=None)

# Fit the factor model to your data
fa.fit(data_Usage_Factor_Final)

# Get the component matrix
component_matrix = pd.DataFrame(fa.loadings_, index=data_Usage_Factor_Final.columns
                                , columns=[f'Factor {i + 1}' for i in range(n_factors)])

# Display the component matrix
print("Component Matrix:")
component_matrix.style.applymap(highlight_cell)
Component Matrix:
Out[138]:
  Factor 1 Factor 2 Factor 3
Feature      
All my friends use 0.537597 -0.496864 0.366977
Old fashioned 0.554361 -0.489050 0.373882
Prestige 0.611817 -0.483032 0.191274
Secure 0.363905 0.608528 0.319025
Communication 0.242821 0.670603 0.394128
Call Friends and Family 0.400629 0.569667 0.237922
Mostly use SMS 0.630501 0.127568 -0.299786
Music or Videos 0.696094 0.113261 -0.471810
Games 0.656758 0.094357 -0.473924

4.1.5 Rotated Factor Solution¶

From the Rotated Component Matrix we note that the variables are partitioned into three mutually exclusive groups. Now it is easy to interpret the factors. The first factor can be treated as a show-off factor, the second factor can be treated as an entertainment factor, and the third factor can be treated as a communication factor.

In [139]:
import pandas as pd
from factor_analyzer import FactorAnalyzer

# Assuming 'data_HS_Factor' is your DataFrame
# Specify the number of factors
n_factors = 3

# Create a FactorAnalyzer object
fa = FactorAnalyzer(n_factors, method='principal', rotation='varimax')

# Fit the factor model to your data
fa.fit(data_Usage_Factor_Final)

# Get the component matrix
component_matrix = pd.DataFrame(fa.loadings_, index=data_Usage_Factor_Final.columns
                                , columns=[f'Factor {i + 1}' for i in range(n_factors)])

# Display the component matrix
print("Rotated Component Matrix:")
component_matrix.style.applymap(highlight_cell)
Rotated Component Matrix:
Out[139]:
  Factor 1 Factor 2 Factor 3
Feature      
All my friends use 0.066050 -0.004167 0.816196
Old fashioned 0.074297 0.011462 0.824998
Prestige 0.241698 -0.058092 0.763173
Secure 0.120439 0.768087 0.006860
Communication -0.009493 0.812119 -0.066175
Call Friends and Family 0.197324 0.708913 0.011717
Mostly use SMS 0.673090 0.168395 0.149230
Music or Videos 0.836347 0.091437 0.110194
Games 0.807231 0.061627 0.097046

From the above discussion we note that the factors are clearly defined. we note that the variables

  • Old fashioned (0.825)
  • All my friends use (0.816)
  • Prestige (0.763)

are loaded on the first factor. We can say that these variables can be grouped into a meaningful factor.

Variables loading on the second factor are:

  • Music or Videos (0.836)
  • Games (0.807)
  • Mostly use SMS (0.673) These variables can form a meaningful factor.

Variables loaded on the third factor are:

  • Communication (0.812)
  • Secure (0.768)
  • Call Friends and Family (0.709) These variables can also grouped into a single factor.

4.1.6 Conclusion¶

We may conclude that the students are mainly using a mobile for show-off and entertainment rather than communication.